Dissertations / Theses: 'Distributed-lag model'

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Mintz, Samuel. "An Almon distributed-lag model of transport investments and agricultural development in Liberia, 1950-1980." Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/75960.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1985.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.
Bibliography: leaves 204-216.
by Samuel Mintz.
Ph.D.

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Torres, Luís Filipe Nunes Pardal Esteves. "Modelling the demand for military expenditure in Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2013. http://hdl.handle.net/10400.5/6540.

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Mestrado em Economia
Throughout history, countries from all over the world have devoted a considerable amount of resources to produce security. This evidence has motivated a growing number of studies that examine the determinants of the demand for military expenditure. Albeit the difficulty to develop a general theoretical framework and the inexistence of a standard empirical approach to model the demand for military expenditure, it is an important issue to understand which factors may influence the military expenditure demand function of a country. The aim of this dissertation is to find out the main variables affecting the Portuguese military expenditure taking into account a comprehensive set of economic, strategic and political determinants. For this goal, a military expenditures demand model is constructed for the period 1960–2010 employing the Autoregressive Distributed Lag (ARDL) bound testing cointegration approach. The results suggest that the Portuguese defence spending is determined by the country´s economic performance, allies‟ defence speeding and security considerations. As far as the domestic political environment is concerned, the dominant ideology of the party in power seems to be insignificant, while the transition to a democratic regime is considered a relevant determinant with a negative effect on the military expenditure.
Ao longo da história, países de todo o mundo têm empenhado uma quantidade considerável de recursos para produzir segurança. Esta constatação tem motivado um número crescente de estudos sobre as possíveis variáveis explicativas da despesa militar. Apesar da dificuldade em estabelecer um quadro teórico de referência e da inexistência de uma abordagem empírica padronizada para determinar a procura de despesa militar, revela-se importante compreender quais as variáveis que influenciam a despesa militar de um país. O objetivo deste trabalho é aferir quais as principais fatores que poderão determinar a despesa militar de Portugal, tendo em conta um amplo conjunto de variáveis de natureza económica, estratégica e política. A prossecução deste objetivo assenta na construção de uma equação de procura para a despesa militar portuguesa, para o período compreendido entre 1960 e 2010, através de um modelo uniequacional ARDL. Os resultados obtidos sugerem que a despesa militar em Portugal é determinada pelo desempenho económico, pelo gasto militar de países aliados e por considerações relativas à perceção das condições de segurança. No que respeita à influência do ambiente político, a ideologia dominante do partido em funções no Governo surge como não significante, ao passo que a transição para um regime democrático é considerada uma variável relevante, com um efeito negativo sobre as despesas militares.

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Yansane, Alfa Ibrahim Mouke. "Statistical Methods for Panel Studies with Applications in Environmental Epidemiology." Thesis, Harvard University, 2011. http://dissertations.umi.com/gsas.harvard:10049.

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Pollution studies have sought to understand the relationships between adverse health effects and harmful exposures. Many environmental health studies are predicated on the idea that each exposure has both acute and long term health effects that need to be accurately mapped. Considerable work has been done linking air pollution to deleterious health outcomes but the underlying biological pathways and contributing sources remain difficult to identify. There are many statistical issues that arise in the exploration of these longitudinal study designs such as understanding pathways of effects, addressing missing data, and assessing the health effects of multipollutant mixtures. To this end this dissertation aims to address the afore mentioned statistical issues. Our first contribution investigates the mechanistic pathways between air pollutants and measures of cardiac electrical instability. The methods from chapter 1 propose a path analysis that would allow for the estimation of health effects according to multiple paths using structural equation models. Our second contribution recognizes that panel studies suffer from attrition over time and the loss of data can affect the analysis. Methods from Chapter 2 extend current regression calibration approaches by imputing missing data through the use of moving averages and assumed correlation structures. Our last contribution explores the use of factor analysis and two-stage hierarchical regression which are two commonly used approaches in the analysis of multipollutant mixtures. The methods from Chapter 3 attempt to compare the performance of these two existing methodologies for estimating health effects from multipollutant sources.

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Qiao, Zhen. "Assessment of the mortality displacement in temperature-related deaths in Brisbane, Australia." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76280/1/Zhen_Qiao_Thesis.pdf.

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This thesis is a population-based ecological study designed to investigate the issue of mortality displacement (or "harvesting" effect) in the assessment of temperature-related deaths in Brisbane, Australia. It examines the temperature impacts on mortality, and assesses the harvesting effects on the temperature–related deaths. This study contributes to the knowledge base of understanding the temperature-mortality relationship and assists in formulating and evaluating public health intervention strategies within the context of climate change.

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Yu, Weiwei. "The identification and quantification of temperature-related mortality." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48208/1/Weiwei_Yu_Thesis.pdf.

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The relationship between weather and mortality has been observed for centuries. Recently, studies on temperature-related mortality have become a popular topic as climate change continues. Most of the previous studies found that exposure to hot or cold temperature affects mortality. This study aims to address three research questions: 1. What is the overall effect of daily mean temperature variation on the elderly mortality in the published literature using a meta-analysis approach? 2. Does the association between temperature and mortality differ with age, sex, or socio-economic status in Brisbane? 3. How is the magnitude of the lag effects of the daily mean temperature on mortality varied by age and cause-of-death groups in Brisbane? In the meta-analysis, there was a 1-2 % increase in all-cause mortality for a 1ºC decrease during cold temperature intervals and a 2-5% increase for a 1ºC increment during hot temperature intervals among the elderly. Lags of up to 9 days in exposure to cold temperature intervals were statistically significantly associated with all-cause mortality, but no significant lag effects were observed for hot temperature intervals. In Brisbane, the harmful effect of high temperature (over 24ºC) on mortality appeared to be greater among the elderly than other age groups. The effect estimate among women was greater than among men. However, No evidence was found that socio-economic status modified the temperature-mortality relationship. The results of this research also show longer lag effects in cold days and shorter lag effects in hot days. For 3-day hot effects associated with 1°C increase above the threshold, the highest percent increases in mortality occurred among people aged 85 years or over (5.4% (95% CI: 1.4%, 9.5%)) compared with all age group (3.2% (95% CI: 0.9%, 5.6%)). The effect estimate among cardiovascular deaths was slightly higher than those among all-cause mortality. For overall 21-day cold effects associated with a 1°C decrease below the threshold, the percent estimates in mortality for people aged 85 years or over, and from cardiovascular diseases were 3.9% (95% CI: 1.9%, 6.0%) and 3.4% (95% CI: 0.9%, 6.0%), respectively compared with all age group (2.0% (95% CI: 0.7%, 3.3%)). Little research of this kind has been conducted in the Southern Hemisphere. This PhD research may contribute to the quantitative assessment of the overall impact, effect modification and lag effects of temperature variation on mortality in Australia and The findings may provide useful information for the development and implementation of public health policies to reduce and prevent temperature-related health problems.

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Bjerknesli, Christoffer. "Effectiveness of monetary policies : A study of the Swedish repo rate between 1994 and 2019." Thesis, Högskolan Dalarna, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34375.

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The repo rate, which is the key interest rate, set by the central banks has been declining for many years and hitting zero in Sweden in late 2014. We analyse the effectiveness on the economy from a change in the repo rate, comparing two time periods with high and low repo rate environments. We use quarterly data on GDP and its components, between 1994 and 2019. For analysing the effectiveness, we use multiple Auto Regressive Distributed Lag (ARDL) modelling to compute a total of 12 models. In our findings, we saw that the effectiveness of a change in repo rate has been increased in the low repo rate environment, making it harder to increase the rate without harming the economy but also increasing the effect of a decrease in the repo rate. Also, we found that the investment component of GDP may exhibit extra high effectiveness in the low repo rate environment. This method of analysing the repo rates impact on the economy could be used for decision makers regarding monetary policies.

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Louw, Riëtte. "Forecasting tourism demand for South Africa / Louw R." Thesis, North-West University, 2011. http://hdl.handle.net/10394/7607.

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Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations.
Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

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8

Ye, Xiaofang. "The effects of hot and cold temperatures on emergency hospital admissions in Brisbane, Australia." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63667/1/Xiaofang_Ye_Thesis.pdf.

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As Earth's climate is rapidly changing, the impact of ambient temperature on health outcomes has attracted increasing attention in the recent time. Considerable number of excess deaths has been reported because of exposure to ambient hot and cold temperatures. However, relatively little research has been conducted on the relation between temperature and morbidity. The aim of this study was to characterize the relationship between both hot and cold temperatures and emergency hospital admissions in Brisbane, Australia, and to examine whether the relation varied by age and socioeconomic factors. It aimed to explore lag structures of temperature–morbidity association for respiratory causes, and to estimate the magnitude of emergency hospital admissions for cardiovascular diseases attributable to hot and cold temperatures for the large contribution of both diseases to the total emergency hospital admissions. A time series study design was applied using routinely collected data of daily emergency hospital admissions, weather and air pollution variables in Brisbane during 1996–2005. Poisson regression model with a distributed lag non-linear structure was adopted to assess the impact of temperature on emergency hospital admissions after adjustment for confounding factors. Both hot and cold effects were found, with higher risk of hot temperatures than that of cold temperatures. Increases in mean temperature above 24.2oC were associated with increased morbidity, especially for the elderly ≥ 75 years old with the largest effect. The magnitude of the risk estimates of hot temperature varied by age and socioeconomic factors. High population density, low household income, and unemployment appeared to modify the temperature–morbidity relation. There were different lag structures for hot and cold temperatures, with the acute hot effect within 3 days after hot exposure and about 2-week lagged cold effect on respiratory diseases. A strong harvesting effect after 3 days was evident for respiratory diseases. People suffering from cardiovascular diseases were found to be more vulnerable to hot temperatures than cold temperatures. However, more patients admitted for cardiovascular diseases were attributable to cold temperatures in Brisbane compared with hot temperatures. This study contributes to the knowledge base about the association between temperature and morbidity. It is vitally important in the context of ongoing climate change. The findings of this study may provide useful information for the development and implementation of public health policy and strategic initiatives designed to reduce and prevent the burden of disease due to the impact of climate change.

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9

Allen, Michael James. "An Evaluation of Seasonality through Four Delineation Methods: A Comparison of Mortality Responses and the Relationship with Anomalous Temperature Events." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1405326473.

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10

Banu, Shahera. "Examining the impact of climate change on dengue transmission in the Asia-Pacific region." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/66387/1/Shahera_Banu_Thesis.pdf.

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Dengue fever (DF) is a serious public health concern in many parts of the world. An increase in DF incidence has been observed globally over the past decades. Multiple factors including urbanisation, increased international travels and global climate change are thought to be responsible for increased DF. However, little research has been conducted in the Asia-Pacific region about the impact of these changes on dengue transmission. The overarching aim of this thesis is to explore the spatiotemporal pattern of DF transmission in the Asia-Pacific region and project the future risk of DF attributable to climate change. Annual data of DF outbreaks for sixteen countries in the Asia-Pacific region over the last fifty years were used in this study. The results show that the geographic range of DF in this region increased significantly over the study period. Thailand, Vietnam and Laos were identified as the highest risk areas and there was a southward expansion observed in the transmission pattern of DF which might have originated from Philippines or Thailand. Additionally, the detailed DF data were obtained and the space-time clustering of DF transmission was examined in Bangladesh. Monthly DF data were used for the entire country at the district level during 2000-2009. Dhaka district was identified as the most likely DF cluster in Bangladesh and several districts of the southern part of Bangladesh were identified as secondary clusters in the years 2000-2002. In order to examine the association between meteorological factors and DF transmission and to project the future risk of DF using different climate change scenarios, the climate-DF relationship was examined in Dhaka, Bangladesh. The results show that climate variability (particularly maximum temperature and relative humidity) was positively associated with DF transmission in Dhaka. The effects of climate variability were observed at a lag of four months which might help to potentially control and prevent DF outbreaks through effective vector management and community education. Based on the quantitative assessment of the climate-DF relationship, projected climate change will likely increase mosquito abundance and activity and DF in this area. Assuming a temperature increase of 3.3oC without any adaptation measures and significant changes in socio-economic conditions, the consequence will be devastating, with a projected annual increase of 16,030 cases in Dhaka, Bangladesh by the end of this century. Therefore, public health authorities need to be prepared for likely increase of DF transmission in this region. This study adds to the literature on the recent trends of DF and impacts of climate change on DF transmission. These findings may have significant public health implications for the control and prevention of DF, particularly in the Asia- Pacific region.

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Попова, З. В. "Економетричне моделювання корупційних правопорушень та їх вплив на економіку країни." Master's thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/72349.

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У роботі досліджено сутність корупційних правопорушень та загального явища корупції на трьох просторово-територіальних рівнях функціонування держави. У ході дослідження були використані просторові індекси Морана, регресійне моделювання та лагові моделі Алмона, а також модифікована гравітаційна модель Дж.Волкера.
The master’s thesis focuses on the essence of corruption offenses and general phenomenon of corruption through the three levels of the government functioning. Thesis includes Moran's I as a measure of spatial autocorrelation, regression model and Almon distributed lag model, as well as modified Walker gravitational model.

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Guo, Yuming. "Estimating the effects of ambient temperature on mortality : methodological challenges and proposed solutions." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59970/1/Yuming_Guo_Thesis.pdf.

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The health impacts of exposure to ambient temperature have been drawing increasing attention from the environmental health research community, government, society, industries, and the public. Case-crossover and time series models are most commonly used to examine the effects of ambient temperature on mortality. However, some key methodological issues remain to be addressed. For example, few studies have used spatiotemporal models to assess the effects of spatial temperatures on mortality. Few studies have used a case-crossover design to examine the delayed (distributed lag) and non-linear relationship between temperature and mortality. Also, little evidence is available on the effects of temperature changes on mortality, and on differences in heat-related mortality over time. This thesis aimed to address the following research questions: 1. How to combine case-crossover design and distributed lag non-linear models? 2. Is there any significant difference in effect estimates between time series and spatiotemporal models? 3. How to assess the effects of temperature changes between neighbouring days on mortality? 4. Is there any change in temperature effects on mortality over time? To combine the case-crossover design and distributed lag non-linear model, datasets including deaths, and weather conditions (minimum temperature, mean temperature, maximum temperature, and relative humidity), and air pollution were acquired from Tianjin China, for the years 2005 to 2007. I demonstrated how to combine the case-crossover design with a distributed lag non-linear model. This allows the case-crossover design to estimate the non-linear and delayed effects of temperature whilst controlling for seasonality. There was consistent U-shaped relationship between temperature and mortality. Cold effects were delayed by 3 days, and persisted for 10 days. Hot effects were acute and lasted for three days, and were followed by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. It is still unclear whether spatiotemporal models using spatial temperature exposure produce better estimates of mortality risk compared with time series models that use a single site’s temperature or averaged temperature from a network of sites. Daily mortality data were obtained from 163 locations across Brisbane city, Australia from 2000 to 2004. Ordinary kriging was used to interpolate spatial temperatures across the city based on 19 monitoring sites. A spatiotemporal model was used to examine the impact of spatial temperature on mortality. A time series model was used to assess the effects of single site’s temperature, and averaged temperature from 3 monitoring sites on mortality. Squared Pearson scaled residuals were used to check the model fit. The results of this study show that even though spatiotemporal models gave a better model fit than time series models, spatiotemporal and time series models gave similar effect estimates. Time series analyses using temperature recorded from a single monitoring site or average temperature of multiple sites were equally good at estimating the association between temperature and mortality as compared with a spatiotemporal model. A time series Poisson regression model was used to estimate the association between temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and Los Angeles, United States during 1987–2000. Temperature change was calculated by the current day's mean temperature minus the previous day's mean. In Brisbane, a drop of more than 3 �C in temperature between days was associated with relative risks (RRs) of 1.16 (95% confidence interval (CI): 1.02, 1.31) for non-external mortality (NEM), 1.19 (95% CI: 1.00, 1.41) for NEM in females, and 1.44 (95% CI: 1.10, 1.89) for NEM aged 65.74 years. An increase of more than 3 �C was associated with RRs of 1.35 (95% CI: 1.03, 1.77) for cardiovascular mortality and 1.67 (95% CI: 1.15, 2.43) for people aged < 65 years. In Los Angeles, only a drop of more than 3 �C was significantly associated with RRs of 1.13 (95% CI: 1.05, 1.22) for total NEM, 1.25 (95% CI: 1.13, 1.39) for cardiovascular mortality, and 1.25 (95% CI: 1.14, 1.39) for people aged . 75 years. In both cities, there were joint effects of temperature change and mean temperature on NEM. A change in temperature of more than 3 �C, whether positive or negative, has an adverse impact on mortality even after controlling for mean temperature. I examined the variation in the effects of high temperatures on elderly mortality (age . 75 years) by year, city and region for 83 large US cities between 1987 and 2000. High temperature days were defined as two or more consecutive days with temperatures above the 90th percentile for each city during each warm season (May 1 to September 30). The mortality risk for high temperatures was decomposed into: a "main effect" due to high temperatures using a distributed lag non-linear function, and an "added effect" due to consecutive high temperature days. I pooled yearly effects across regions and overall effects at both regional and national levels. The effects of high temperature (both main and added effects) on elderly mortality varied greatly by year, city and region. The years with higher heat-related mortality were often followed by those with relatively lower mortality. Understanding this variability in the effects of high temperatures is important for the development of heat-warning systems. In conclusion, this thesis makes contribution in several aspects. Case-crossover design was combined with distribute lag non-linear model to assess the effects of temperature on mortality in Tianjin. This makes the case-crossover design flexibly estimate the non-linear and delayed effects of temperature. Both extreme cold and high temperatures increased the risk of mortality in Tianjin. Time series model using single site’s temperature or averaged temperature from some sites can be used to examine the effects of temperature on mortality. Temperature change (no matter significant temperature drop or great temperature increase) increases the risk of mortality. The high temperature effect on mortality is highly variable from year to year.

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Dang, Thi Anh Thu. "Impact of ambient temperature on hospital admissions for acute myocardial infarction in central coast of Vietnam." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123901/1/Thi%20Anh%20Thu_Dang_Thesis.pdf.

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Globally, there is evidence that extremes in temperature linked with climate change can exacerbate underlying health conditions and lead to hospitalization and premature death. This study examined the short-term effects of ambient temperature extremes on hospital admissions due to acute myocardial infarction (AMI) in three geographically dispersed provinces along the Central Coast region of Vietnam. We found that risk of AMI admission is associated with high and low temperatures, in part due to variation in sub-regional climate. Public health preparedness and multi-level interventions in communities and workplaces including factories and farms should attempt to reduce people's exposure to extreme temperature.

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Пігуль, Є. І., and Ye I. Pihul. "Моделювання впливу цифровізації на розвиток фінансових технологій." Master's thesis, Сумський державний університет, 2021. https://essuir.sumdu.edu.ua/handle/123456789/85061.

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У роботі досліджено детермінанти поширення цифрових технологій у фінансовій сфері, проаналізовано існуючі підходи та методи до моделювання зв’язку між цифровими та фінансовими технологіями. У роботі розроблено науково-методичний підхід до оцінювання цифровізації як драйвера та інгібітора розвитку фінансових технологій на основі побудови регресійних моделей панельних даних та моделі розподіленого лагу.
The determinants of digital technology dissemination in the financial sphere are investigated, the existing approaches and methods to modeling the relationship between digital and financial technologies are analyzed. The paper develops a scientific and methodological approach to the evaluation of digitalization as a driver and inhibitor of the development of financial technologies based on the construction of panel regression models and distributed lag model.

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Král, Ondřej. "Phillipsova křivka z pohledu analýzy časových řad v České republice a Německu." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-360701.

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Government fiscal and monetary policy has long been based on the theory that was neither proven nor refuted since its origination. The original form of the Phillips curve has undergone significant modifications but its relevance remains questionable. This thesis examines the correlation between inflation and unemployment observed in the Czech Republic and Germany over the last twenty years. The validity of the theory is tested by advanced methods of time series analysis in the R environment. All the variables are gradually tested which results in the assessment of the correlation between the time series. The outcome of the testing is presented for both countries and a comparison at international level is drawn. Is is discovered that both of the countries have dependencies in their data. Czech republic has significant dependency in both ways, for Germany is the dependency significantly weaker and only in one way.

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Sagir, Serhat. "Effects Of Monetary Policy On Banking Interest Rates: Interest Rate Pass-through In Turkey." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613717/index.pdf.

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In this study, the effects of CBRT monetary policy decisions on the consumer, automobile, housing and commercial loans of the banks during the period from the early of 2004 to the middle of 2011 are examined. In order to perform this study, it is benefited from weekly weighted average loan interest rate data of the banks, which is the data having the highest frequency that could be obtained from the electronic data distribution system of CBRT. Monetary policy instruments of Central Bank may change in the course of time or monetary policy could be executed by more than one instrument. Therefore, as the political interest rate would be insufficient in the calculation of the effect of monetary policy on loan interest rates of the banks, Government Dept Securities&rsquo
premiums are used instead of the political interest rates in this study to make it reflect the policies of central bank more clearly as a whole. Among the Government Dept Securities that have different maturity structure, benchmark bonds that are adapted to the expected political interest rate changes and that react to the unexpected interest rate changes at the high rate (reaction coefficient 0.983) are used. In order to weight the cointegration relation between interest rates, unrestricted error correction model is established and it is determined by Bound Test that there is a long-term relation between each interest rate and interest rate of benchmark bond. After a cointegration relation is determined among the serials, autoregressive distributed lag model is used to determine the level of transitivity and it is determined that monetary policy decisions affect the banking interest rate at 77% level and by 13 weeks delay on average.

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Marçal, Jean Vinícius. "A transmissão da taxa de juros no Brasil sob uma abordagem não linear." Universidade Federal de Juiz de Fora (UFJF), 2017. https://repositorio.ufjf.br/jspui/handle/ufjf/4985.

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Esta dissertação objetivou analisar o mecanismo de transmissão da política monetária para a taxa de juros de varejo na economia brasileira em uma abordagem não linear. O período principal de análise foi de março de 2011 a março de 2016. A estratégia empírica consistiu no emprego da abordagem de política monetária para o repasse e do uso do modelo de cointegração não linear NARDL. Os principais resultados encontrados são que para as taxas de empréstimos analisadas encontrou-se evidência da assimetria de curto e longo prazo no repasse da taxa SELIC. Conclui-se ainda que a transmissão da taxa de juros no Brasil é caracterizada por apresentar o predomínio do sobre repasse. Por fim, ao comparar o período principal com um período anterior, delimitado de janeiro de 2000 a dezembro de 2012, verificou-se a mudança no sinal da assimetria, passando de negativa para positiva no período atual.
This dissertation aims to analyze interest rate pass-through mechanism from SELIC to retail interest rate in the Brazilian economy in a nonlinear framework. The main review period was from March 2011 to March 2016. The empirical strategy consists in the use of monetary policy approach to interest rate pass-through and use of nonlinear cointegration model NARDL. The main results are that exist evidence of short as well as long-term asymmetry in the interest rate pass-through. We can also conclude that the interest rate pass-through is characterized by the predominance of the more complete pass-through. Finally, when comparing the main period with an earlier period, delimited from January 2000 to December 2012, there was a change in the sign of asymmetry, from negative to positive in the current period.

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18

Obermeier, Viola. "Flexible distributed lag models and their application to geophysical data." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-170387.

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Regression models with lagged covariate eﬀects are often used in biostatistical and geo- physical data analysis. In the diﬃcult and all-important subject of earthquake research, strong long-lasting rainfall is assumed to be one of many complex trigger factors that lead to earthquakes. Geophysicists interpret the rain eﬀect with an increase of pore pressure due to the inﬁltra- tion of rain water over a long time period. Therefore, a sensible statistical regression model examining the inﬂuence of rain on the number of earthquakes on day t has to contain rain information of day t and of preceding days t − 1 to t − L. In the ﬁrst part of this thesis, the speciﬁc shape of lagged rain inﬂuence on the number of earthquakes is modeled. A novel penalty structure for interpretable and ﬂexible estimates of lag coeﬃcients based on spline representations is presented. The penalty structure enables smoothness of the resulting lag course and a shrinkage towards zero of the last lag coeﬃcient via a ridge penalty. This additional ridge penalty oﬀers an approach to another problem neglected in previous work. With the help of the additional ridge penalty, a suboptimal choice of the lag length L is no longer critical. We propose the use of longer lags, as our simulations indicate that superﬂuous coeﬃcients are correctly estimated close to zero. We provide a user-friendly implementation of our ﬂexible distributed lag (FDL) ap- proach, that can be used directly in the established R package mgcv for estimation of generalized additive models. This allows our approach to be immediately included in com- plex additive models for generalized responses even in hierarchical or longitudinal data settings, making use of established stable and well-tested algorithms. We demonstrate the performance and utility of the proposed ﬂexible distributed lag model in a case study on (micro-) earthquake data from Mount Hochstaufen, Bavaria with focus on the speciﬁc shape of the lagged rain inﬂuence on the occurrence of earthquakes in diﬀerent depths. The complex meteorological and geophysical data set was collected and provided by the Geophysical Observatory of the Ludwig-Maximilians University Munich. The beneﬁt of ﬂexible distributed lag modeling is shown in a detailed simulation study. In the second part of the thesis, the penalization concept is extended to lagged non- linear covariate inﬂuence. Here, we extend an approach of Gasparrini et al. (2010), that was up to now unpenalized. Detailed simulation studies illustrate again the beneﬁts of the penalty structure. The ﬂexible distributed lag nonlinear model is applied to data of the volcano Merapi in Indonesia, collected and provided by the Geophysical Observatory in Fürstenfeldbruck. In this data set, the speciﬁc shape of lagged rain inﬂuence on the occurrence of block and ash ﬂows is examined.

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19

Zewdie, Worku, and E. Csaplovics. "Assessment of rainfall and NDVI anomalies in semi-arid regions using distributed lag models." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A34790.

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The semiarid regions of Ethiopia are exposed to anthropogenic and natural calamities. In this study, we assessed the relationship between Tropical Applications of Meteorology using Satellite data (TAMSAT) and MODIS Normalized Difference Vegetation Index (NDVI) data for the period 2000 to 2014 on decadal and annual basis using multivariate distributed lag (DL) models. Decadal growing season (June to September) values for kaftahumera were calculated from MODIS NDVI data. The growing season NDVI values are highly correlated with the precipitations during the whole study period. A lag of up to 30 days observed in most parts of our study region in which the rainfall has effects on vegetation growth after 40 days. The lag-time effects vary with the distribution of land use types and seasons. A lower correlation was observed in the woodland regions where significant deforestation occurred due to expansion of croplands. The loss in vegetation contributed to the low biomass production attributable to extended loss in vegetation cover.

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20

Hu, Wenbiao. "Applications of Spatio-temporal Analytical Methods in Surveillance of Ross River Virus Disease." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16109/1/Wenbiao_Hu_Thesis.pdf.

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The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.

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21

Hu, Wenbiao. "Applications of Spatio-temporal Analytical Methods in Surveillance of Ross River Virus Disease." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16109/.

Full text

Abstract:

The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.

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22

Ferdi, Fouad. "Dynamique macroéconomique des firmes financiarisées." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCD006.

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La question principale de ce travail de recherche est de déterminer le type du régime de croissance économique des pays dits « avancés ». Pour ce faire, j’étudie le schéma d’accumulation du capital au travers du comportement d’investissement des entreprises non financières, afin d’en établir les conséquences en termes de stabilité économique. Je défends l'idée d'une instauration depuis les années 1980 d'un régime de croissance basé, entre autres, sur le capital intangible et financier, dont j'étudie les caractéristiques et les conséquences sur l'économie. A partir de cette hypothèse, je propose de réviser la théorie du profit d’Adrian Wood (1975) à l’aune de ces transformations institutionnelles récentes, afin d’éclairer les dynamiques méso économiques d’accumulation de capital au sein des grandes firmes multinationales. Cette nouvelle vision de la firme financiarisée et mondialisée, est ensuite confrontée à la théorie d’instabilité financière de Hyman Minsky afin d’apporter des éléments de réponse à la problématique de départ. La croissance induite par ces nouveaux comportements d’investissement et de financement peut-elle s’inscrire dans l’approche minskyenne d’une croissance intrinsèquement instable ? La démarche consiste à établir un lien entre, d’une part, les stratégies d’accumulation du capital global (fixe, financier et intangible) et le levier d’endettement des entreprises avec, d’autre part, les conséquences macroéconomiques de la dynamique d’endettement sur la croissance globale
The main goal of this thesis was to determine the macroeconomic growth regime of advanced economies. Hence, I addressed the non-financial corporation’s capital accumulation schemes in order to establish their macrodynamics as regard to stability issues. It has been argued that the financialization phenomenon has deeply transformed the growth path by changing NFCs’ habits of investment. Following two major institutional mutations, big multinational firms adapted their investment funding process according to the transformation of the international financial system. They increasingly engaged into financial activities to guaranty a better access to capital next to a better short-run profitability for the sake of shareholders’ value maximization. Their financial holding entities, acting as cash hubs, invested in the excess securities resulting from banks’ new paradigm in dealing with debt, i.e. “generate and distribute”. From another stand, another institutional change affected the production process towards the paradigm of “downsize and distribute”. At the end of the day, to stand steady over these two mutating legs (namely production and its funding) NFCs had to keep control over both. From one side, they engaged into intangibles to lead the global value chain and control production, and from the other, into financial investment to optimize their funding capacity

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23

Kang, Shin-jae. "Korea's export performance : three empirical essays." Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/767.

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24

Obermeier, Viola [Verfasser], and Helmut [Akademischer Betreuer] Küchenhoff. "Flexible distributed lag models and their application to geophysical data / Viola Obermeier. Betreuer: Helmut Küchenhoff." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/1052015549/34.

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25

Olfati, Ronak. "The Impact of Oil Revenue on the Iranian Economy." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16834.

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This study aims to identify the effects of oil income on economic growth in Iran over the period 1955-2014. The empirical literature indicates that countries with natural resources are growing more slowly than their counterparts. However, the results from this literature are far from conclusive, particularly in regard to the role played by oil-rich countries. Needless to say, this role depends on other factors as well, including the political situation in the country, the quality of institutions, and the efficacy of the financial system. Some empirical research has found that natural resources, particularly oil, can have a positive impact on the output of a country. although natural resources are not a factor of production in growth theories, studies have used different growth frameworks in order to discover whether having natural resources is a blessing or a curse. In line with recent studies, this work uses an augmented neoclassical growth model to develop a theoretical framework where oil enters the long-term output of the country through saving and investment. Overall, the results suggests that oil income has a positive impact on the level of output per capita in Iran. The findings of the econometric results are in line with the historical analysis of the study. Since different methods and proxies were used, a total of eight models were estimated. Interestingly, when PRIVY is used as an index of financial development, the result of the study changes and oil no longer has a significant impact on the economy. However, this can be translated to an inefficient allocation of credit to the private sector.

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26

Jacobson, Ludmilla da Silva Viana. "Efeitos adversos da poluição atmosférica em crianças e adolescentes devido a queimadas na Amazônia: uma abordagem de modelos mistos em estudos de painel." Universidade do Estado do Rio de Janeiro, 2013. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=5243.

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Esta tese investiga os efeitos agudos da poluição atmosférica no pico de fluxo expiratório (PFE) de escolares com idades entre 6 e 15 anos, residentes em municípios da Amazônia Brasileira. O primeiro artigo avaliou os efeitos do material particulado fino (PM2,5) no PFE de 309 escolares do município de Alta Floresta, Mato Grosso (MT), durante a estação seca de 2006. Modelos de efeitos mistos foram estimados para toda a amostra e estratificados por turno escolar e presença de sintomas de asma. O segundo artigo expõe as estratégias utilizadas para a determinação da função de variância do erro aleatório dos modelos de efeitos mistos. O terceiro artigo analisa os dados do estudo de painel com 234 escolares, realizado na estação seca de 2008 em Tangará da Serra, MT. Avaliou-se os efeitos lineares e com defasagem distribuída (PDLM) do material particulado inalável (PM10), do PM2,5 e do Black Carbon (BC) no PFE de todos os escolares e estratificados por grupos de idade. Nos três artigos, os modelos de efeitos mistos foram ajustados por tendência temporal, temperatura, umidade e características individuais. Os modelos também consideraram o ajuste da autocorrelação residual e da função de variância do erro aleatório. Quanto às exposições, foram avaliados os efeitos das exposições de 5hs, 6hs, 12hs e 24hs, no dia corrente, com defasagens de 1 a 5 dias e das médias móveis de 2 e 3 dias. No que se refere aos resultados de Alta Floresta, os modelos para todas as crianças indicaram reduções no PFE variando de 0,26 l/min (IC95%: 0,49; 0,04) a 0,38 l/min (IC95%: 0,71; 0,04), para cada aumento de 10g/m3 no PM2,5. Não foram observados efeitos significativos da poluição no grupo das crianças asmáticas. A exposição de 24hs apresentou efeito significativo no grupo de alunos da tarde e no grupo dos não asmáticos. A exposição de 0hs a 5:30hs foi significativa tanto para os alunos da manhã quanto para a tarde. Em Tangará da Serra, os resultados mostraram reduções significativas do PFE para aumentos de 10 unidades do poluente, principalmente para as defasagens de 3, 4 e 5 dias. Para o PM10, as reduções variaram de 0,15 (IC95%: 0,29; 0,01) a 0,25 l/min (IC95%: 0,40 ; 0,10). Para o PM2,5, as reduções estiveram entre 0,46 l/min (IC95%: 0,86 to 0,06 ) e 0,54 l/min (IC95%: 0,95; 0,14). E no BC, a redução foi de aproximadamente 0,014 l/min. Em relação ao PDLM, efeitos mais importantes foram observados nos modelos baseados na exposição do dia corrente até 5 dias passados. O efeito global foi significativo apenas para o PM10, com redução do PFE de 0,31 l/min (IC95%: 0,56; 0,05). Esta abordagem também indicou efeitos defasados significativos para todos os poluentes. Por fim, o estudo apontou as crianças de 6 a 8 anos como grupo mais sensível aos efeitos da poluição. Os achados da tese sugerem que a poluição atmosférica decorrente da queima de biomassa está associada a redução do PFE de crianças e adolescentes com idades entre 6 e 15 anos, residentes na Amazônia Brasileira.
This thesis investigates the acute effects of air pollution on peak expiratory flow (PEF) of schoolchildren between the ages of 6 and 15, living in Brazilian Amazon municipalities. The first article evaluated the effects of fine particulate matter (PM2.5) on PEF of 309 schoolchildren in the municipality of Alta Floresta, Mato Grosso (MT), during the dry season in 2006. Mixed effect models were estimated for the whole sample and stratified by the time of the day children attended school, and also by the presence of asthma symptoms. The second article describes the strategies used to determine the random error variance function of mixed effect models. The third one analyzes the data of the panel study with a sample of 234 schoolchildren carried out in Tangará da Serra, MT, during the dry season in 2008. Linear effects and the ones with distributed lag (PDLM) of inhalable particulate matter (PM10), PM2.5 and Black Carbon (BC) were assessed for the whole sample and stratified by age. In all three articles, the mixed effect models were adjusted by time trend, temperature, humidity and personal characteristics. The models also considered the adjustment of the residual autocorrelation and of the random error variance function. Regarding the exposures, its effects were evaluated in 5hs, 6hs, 12hs and 24hs, on the current day, with lags of 1 to 5 days and moving averages of 2 and 3 days. According to results in Alta Floresta, the models for all the children indicated reductions in the PEF varying from 0.26 l/min (CI95%: 0.49; 0.04) to 0.38 l/min (CI95%: 0.71; 0.04), for each increase of 10g/m3 on PM2.5. Significant effects of pollution were not observed in the group of asthmatic children. The 24-hour exposure presented significant effects in the group of students who attended school in the afternoon and in the group of non-asthmatic ones. The exposure from midnight to 5:30 A.M. was significant both to students who attended school in the morning and the ones who studied in the afternoon. In Tangará da Serra, the results showed significant reductions on the PEF for increases of 10 units of pollutants, mainly for lagged exposures of 3, 4 and 5 days. For PM10, the reductions varied from 0.15 (CI95%: 0.29; 0.01) to 0.25 l/min (CI95%: 0.40; 0.10). For PM2.5, the reductions ranged from 0.46 l/min (CI95%: 0.86 to 0.06) to 0.54 l/min (CI95%:0.95; 0.14). And for BC, the reduction was about 0.014 l/min. In relation to PDLM, more important effects were noticed in models based on the exposure of the current day until 5 past days. The global effect was significant only for PM10, with PEF reduction of 0.31 l/min (CI95%: 0.56; 0.05). This approach also indicated significant lagged effects for all pollutants. In the end, this study observed that the children between 6 and 8 years old were the most vulnerable to pollution effects. These findings in the thesis suggest that air pollution due to biomass burning is associated to PEF reduction in children and teenagers between the ages of 6 and 15, living in the Brazilian Amazon.

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27

Huang, Xing-Yi, and 黃馨儀. "Multivariate Approaches for the Distributed Lag Non-Linear Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/355efz.

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碩士
國立陽明大學
公共衛生研究所
107
Due to the impact of global climate changes, extreme temperature occurs more frequently. As a result, further studies focus on the relationship between temperature and health. The Distributed Lag Non-Linear Model (DLNM), a framework that can simultaneously assess the non‐linear exposure–response dependencies and delayed effects, has been widely adopted in environmental epidemiology and public health to evaluate the association between weather and mortality all over the world. However, the lagged health outcome has not been reported with the delayed effects of exposures. In this thesis, we propose several new statistical methods to investigate the lagged association in both the health outcome and exposures. According to the longitudinal data in Taipei from 2012 to 2016, which recorded daily mortality, temperature and air pollutions, for the number of daily deaths in the previous periods, we evaluated the performance of proposed models and compare to the DLNM results. Based on computer simulations and real data analyses, even if most methods are more significant than the DLNM, type-I errors are invalid and could be highly inflated. The best model is log(

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Sung, Meng-Hsuan, and 宋孟軒. "A Simulation Study of Polynomial Distributed Lag Model, Distributed Lag Non-Linear Model, and Poisson Regression Model based on Longitudinal climate and mortality data in Taiwan." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/22464810541124397796.

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碩士
國立陽明大學
公共衛生研究所
104
In recent years, the effects of meteorological factors on health outcomes have gained popularity because of the observed and predicted climate change, which is expected to influence a general rise in temperature but also the abnormal climatic extremes. Numerous studies in environmental epidemiology and public health used different statistical models to quantify the relationship between temperature and mortality, however, none of them compared the overall significance and type-I errors in different models. In this thesis, polynomial distributed lag model(PDLM), distributed lag non-linear model(DLNM) and Poisson regression model were compared and examined their overall effects under numerous scenarios. The longitudinal climate data in Taiwan from 1994 to 2008 were used for permutation studies, such that type-I error of PDLM and DLNM could be tested by using unrelated structure between temperature and mortality. In addition, power of PDLM, DLNM and Poisson regression model could be evaluated through adapting the data simulated from specific distribution such as normal and Poisson. The simulation results suggest that DLNM had a stable performance whether comparing in type one error or power. Finally, the longitudinal data were further used in DLNM to discuss short-term effects of temperature on mortality.

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Chen, E.-Wen, and 陳玉玟. "The Determinants of Capital Structure-The Use of Distributed Lag Model." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/67590716997966283389.

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碩士
國立中興大學
會計學研究所
83
The purpose of this paper is to examine the determinants of capital structure. Moreover, this study also investigates Whether or not the determinants have delayed effects on the capital structure choice. 　　A distributed lag regression is employed with the financial data drawing from the companies listed in the Taiwan stock market to explore the relationships between a firm''s capital structure and its determinants. The potential determinants include corporate tax rate, the non-debt tax shelter, future growth opportunities, firm size, fixed asset ratio, stock returns, inflation rate, systematic risk, nonsystematic risk,and regulation. 　　Our regression results show that regulation has a strong positive effect on the long-term and total debt capacity. Accordingly, regulation appears to be an important determinant of capital structure. Our regression results also reveal that corporate tax rate, the non-debt tax shelter, firm size, and stock returns appear to be important determinants of capital atructure. The corporate tax rate and firm size have significantly positive impacts on the leverage ratio. The non-debt tax shelter and stock returns have significantly negative impacts on the leverage ratio.

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Chao, Jen-Hsiu, and 趙仁秀. "Relative Price Dispersion and Inflation：A GARCH-in-Mean Model with Distributed Lag." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/98551558537784226201.

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碩士
世新大學
經濟學系
92
Using monthly wholesale price index（WPI）data of Taiwan’s manufacturing sector to test the relation between inflation and relative-price dispersion(RPD).At the same time, we consider the effects of distributed lags of inflation rate on RPD and the problems of asymmetry. We find that one-period lag of RPD（AR(1)） dominates final results. The empirical result supports for both menu cost and signal extraction models when lagged RPD was excluded from dependent variables. On the other hand, the asymmetric effect of deflation exists only when lagged RPD was removed. If lagged RPD was included, the result will differ. The results imply monetary policy with inflation rate targeting may decrease aggregate real shock of inflation rate when the result supports for both menu cost and signal extraction models.

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Wu, Chung-Li, and 吳崇立. "Permutation-Based Type I Error Study of Distributed Lag Non-Linear Model for Associations between Temperature and Mortality." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/70657127198327894949.

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碩士
國立陽明大學
公共衛生研究所
103
Objectives: In environmental epidemiology, distributed lag non-linear model (DLNM) has been widely adapted to examine if there is an association between the environmental factor and the health outcome. The feature of this model is that- it not only considers the lag effect between the environmental factor and the health outcome, but also takes into account the non-linear relationship. However, it is uncertain how many lag terms are needed to declare significance at the same time to conclude the lag effect. The aim of - this thesis is to realize how the family-wise error rate (FWER) varies in different settings of the parameters, and also compare the results with the adjusted p-value after Bonferroni’s correction. Methods: We implemented the permutation test of the distributed lag non-linear model to examine the association between temperature and mortality using the longitudinal data in Taipei, Taiwan. Results: Although the results vary with respect to different settings (lag and numbers of knots), our conclusions provide a simple guideline for concluding the overall significance according to the simulation results. Conclusions: Based on our results, the number of - lags needed to conclude the lag effect may varies in different settings of parameters. We also applied the DLNM to the longitudinal data in Taiwan and confirmed the association between temperature and mortality. Keywords: Distributed lag non-linear models, Family-wise error rate, Multiple testing, Permutation test

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Gao, Bo 1988. "Stress Testing Projected Capitalized Farmland Values." Thesis, 2012. http://hdl.handle.net/1969.1/148223.

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This study initially presents historical trends in both the capitalized value and market value of farmland in the eight states comprising the Corn Belt and Lake States production regions as defined by the USDA. An econometric analysis of annual real cash rents per acre prior to determining the capitalized value of farmland in the eight states is then conducted. Two distributed lag models were hypothesized. The comparison of regression results of these two distributed lag models indicates that current year real cash rent can be best explained by current year real net farm income, lagged real net farm income over a period of years, and real cash rent in the previous year. A spreadsheet simulation model is used to project capitalized farmland values in each state as well as regional averages over the 2012-2015 period. These projections reflect alternative assumptions regarding future trends in real net farm income at the state level as well as the rate on 10-year constant maturity U.S. government bonds to assess the potential sensitivity of capitalized farmland values under adverse economic conditions. The projected trends in capitalized farmland values under two alternative stress scenarios reflecting higher interest rates levels and lower net farm income levels indicates that capitalized farmland values are particularly sensitive to interest rate fluctuations since cash rent expectations of landlords are based on current and lagged historical profit performance.

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Chiliba, Laston. "A re-examination of the exchange rate overshooting hypothesis: evidence from Zambia." Thesis, 2014. http://hdl.handle.net/10539/15273.

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Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2014.
Dornbusch’s exchange rate overshooting hypothesis has guided monetary policy conduct for many years though empirical evidence on its validity is mixed. This study re-examines the validity of the overshooting hypothesis by using the autoregressive distributed lag (ARDL) procedure. Specifically, the study investigates whether the overshooting hypothesis holds for the United States Dollar/Zambian Kwacha (USD-ZMK) exchange rate. In addition, the study tests if there is a long-run equilibrium relationship between the USD-ZMK exchange rate and the macroeconomic fundamentals (money supply, real Gross Domestic Product (GDP), interest rates and inflation rates). The study uses monthly nominal USD/ZMK exchange rates and monetary fundamentals data from January 2000 to December 2012. The study finds no evidence of exchange rate overshooting. The result also show that there is no long run equilibrium relationship between the exchange rate and the differentials of macroeconomic fundamentals. The implication is that macroeconomic fundamentals are insignificant in determining the exchange rate fluctuations in the long run. This finding is inconsistent with the monetary model of exchange rate determination, which asserts that there is a long-run relationship between the exchange rate and macroeconomic fundamentals.

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Yusof, Yusniliyana. "Socio-economic Development and the Role of Fiscal Decentralisation in Malaysia." Phd thesis, 2018. http://hdl.handle.net/1885/148710.

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Malaysia is one of the countries in the world that has adopted a unique system of governance that involves monarchy, democracy and federal system. Nevertheless, all the thirteen states are governed by employing a federal governance structure headed by the Prime Minister. Given the federal structure of Malaysian economy, it is logical to expect the variations in the socio-economic development across the states. It is interesting and also important to understand the force behind the variations across the performance of the states. This thesis first identifies the significant factors that influence the variation in economic growth across the states, which is the core factor determining socio-economic development. Next, the thesis highlights the influence of the federal system on the development expenditure of the states, which is crucial for socio-economic development. Finally, the thesis examines the impact of decentralisation on transferring the Malaysian economy from the middle-income country to high-income country. The following paragraphs briefly explain how the above three main analyses have been carried out in this thesis. In Chapter 2, the study contributes to the aim of regional development policy in reducing regional disparities, by examining the spatial balance in socio-economic development across the states of Malaysia based on few selected socio-economic indicators. Besides, the study has attempted to understand the issues in the development gaps across the Malaysian states by evaluating the factors that explained the variation in economic growth. Though the pattern in the spatial socio-economic imbalance demonstrates a decreasing trend, the development index reveals that performance of less developed states remained behind that of the developed states for more than a decade. Based on three-stage least squares (3SLS) estimation technique, all independent variables in the main equation are significant to explain the development gaps within the states that covers the period between 2005 and 2015. The significant factors in explaining the variation in growth across the Malaysian states are relating to agriculture, manufacturing, human capital, population growth, Chinese ethnic, institutional factors and natural resources. In Chapter 3, the study examines whether there is convergence in development expenditure across Malaysian states and investigates the importance of decentralisation in affecting the pattern of development expenditure during the short run and long run. The convergence analysis involved the data of annual growth for the short run, and average three-year and five-year growth for the long run from 2000 to 2015. The study uses panel data approaches of pooled OLS, fixed effects and random effects estimation procedures. The findings provide empirical evidence on the development expenditure convergence within the states during both short run and long run. It is also found that all fiscal decentralisation indicators (state per capita revenue, state-sourced per capita revenue, state-sourced revenue as a share of total revenue and state-sourced capacity as a share of the national average) are imperative in influencing the fiscal behaviour of state governments in Malaysia. The assistance from the federal government through transfer payment is needed to strengthen the expenditure capacity of Malaysian states. In Chapter 4, the study inspects the role of fiscal decentralisation as a solution for escaping from the middle-income trap. The study employed annual time series data from 1985 to 2015. The Autoregressive Distributed Lag (ARDL) bounds test reveals the presence of long run relationship between the levels of the dependent variable (economic growth) and the regressors (the participation of federal, state and local governments in the economy, labour force and net exports). The results of the study offer a possible solution that could help Malaysia to escape from the stagnant economic growth. It is found that fiscal decentralisation has a growth effect on Malaysian economy though the benefits of decentralisation are realised differently at different levels of government. The positive impact of revenue decentralisation is realised at the state but not the local level. In contrast, the opposite results are reported in the case of expenditure decentralisation. The benefits of expenditure decentralisation are accomplished at local but not the state level.

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Djoumessi, Emilie Chanceline Kinfack. "Financial development and economic growth : a comparative study between Cameroon and South Africa." Diss., 2009. http://hdl.handle.net/10500/2746.

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The causal relationship between financial development and economic growth is a controversial issue. For developing countries, empirical studies have provided mixed result. This study seeks to empirically explore the relationship and the causal link between financial development and economic growth in two sub-Saharan African countries between 1970 and 2006. The empirical investigation is carried out using time methods and the five most commonly used indicators of financial development in the literature. However, the causal relationship was carried out using two different methods which are the autoregressive distributed lag bounds testing (ARDL) and the vector error correction model (VECM). Using this above methodology the study first found that in both countries there is a positive and long-term relationship between all the indicators of financial development and economic growth which was proxied by the real per capita GDP. With respect to the causality test, the two methods used provide mixed results especially in South Africa. In Cameroon the study found that financial development causes economic growth using the two methods, whereas in South Africa economic growth causes financial development when the VECM method is used, while there is an independence relationship between the two variables in South Africa when using ARDL.
Economics
M.Comm. (Economics)

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36

Parahara, Withanalage Niroshani Anuruddika Kumari. "Analysis of motives and the impact of foreign remittance on financial development, poverty and income inequality: empirical evidence from Sri Lanka." Thesis, 2019. https://vuir.vu.edu.au/40469/.

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Foreign remittance is the main external source of finance for Sri Lanka. It contributes immensely towards the country’s economy and makes up around 8 per cent of the GDP. However, there is a lack of study on foreign remittance in the Sri Lankan context, which hinders the potential of creating a comprehensive policy on remittance. Hence, this thesis has analysed the motives for foreign remittance and its determinants, the impact of foreign remittance on financial development, alongside its influence on poverty and income inequality in Sri Lanka. The objective of this research was to provide relevant information to the policy makers to guide them in enhancing the benefits to Sri Lanka from foreign remittance. The study used the autoregressive distributed lag (ARDL) and autoregressive (AR) models, Granger causality, impulse response analysis, variance decomposition and recursive estimation for analysing research data. At first, the motive for foreign remittance and its nature (static or dynamic) was examined to investigate the relevance of the prevalent notion that remittance motive is static in nature. Based on recursive estimation, the study found that remittance to Sri Lanka was dominated by altruistic motive until 1992 and by self-interest motive thereafter. Therefore, the findings disproved that the motive for remittance is static and confirmed its dynamic nature. This highlighted the need to assess the motive for foreign remittance at an individual country level and adjust migration and remittance policies accordingly since the motives keep changing over time and require continuous monitoring. The next stage in this study involved determining the key factors of foreign remittance to Sri Lanka by using factor analysis and ARDL model. Through the analysis, it was found that the per capita GDP and government stability are long-run determinants of remittance and have a positive impact on it. In addition, accountability and socio-economic status were identified as short-run determinants. The findings showed the importance and implications of push factors over pull factors to determine the inflow of remittance. It demonstrated that the Sri Lankan migrants, unlike altruistically driven migrants, are highly attentive to economic and political stability, and send more money when the economic and political conditions of the home country are favourable for investment. The undertaken research also examined the impact of foreign remittance on financial development in Sri Lanka using ARDL model. It used four proxies to represent financial development: money, deposits, credit and assets. The analysis revealed a significant impact of remittance on money and credit in Sri Lanka. Furthermore, it showed that the nexus between remittance and financial development supports a complementary hypothesis. This highlighted the likelihood of remittance to enhance the credit availability, promote investment and thereby enhance the economic growth of the country. Finally, the study examined the causal relationships between foreign remittance and poverty, and foreign remittance and income inequality in Sri Lanka with autoregressive model. The analysis showed that foreign remittance has a significant impact on moderate poverty reduction. Apart from the AR model, the Granger causality analysis verified the above-mentioned relationships between foreign remittance and poverty in Sri Lanka. However, the results of the study found no evidence to prove a significant impact or a causal relationship between foreign remittance and income inequality in Sri Lanka, unlike in some developing countries. All the findings from this research contribute to both the theoretical and the empirical literature. They provide relevant information that are invaluable for migration and remittance policy development, which can enable Sri Lanka to create an investment- friendly environment to attract more remittance by reducing the country’s financial risk and by enhancing its economic stability. In addition, since Sri Lankan employment migrants are motivated by self-interest the findings would help the financial institutions to customise their services to migrants, to further enhance their investment motive.

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Tsai, Chen-Wei, and 蔡陳緯. "Permutation Study of Polynomial Distributed Lag Models for associations between Temperature and Mortality in Taiwan." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/71010416204554379209.

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碩士
國立陽明大學
公共衛生研究所
102
In the environmental epidemiology study, polynomial distributed lag models have been used to determine the association between the environmental factor and health outcomes, which is a dynamic regression method in time series analysis for quantifying the relationships between the outcome variable and the lagged values of the explanatory variable. However, it is uncertain how many lag variables are needed to declare significance of the cumulative effect. The aim of this thesis is to address the family-wise error rate (FWER) and evaluate the adjusted p-value by Bonferroni’s correction. Therefore, we implemented the permutation test of the distributed lag model for the association between temperature and mortality in Taiwan. The FWER, k-FWER, as well as the numbers of significant lags are estimated to declare the overall effect. Although the results vary with respect to the different settings of the parameters in the model, our conclusions provide simple guideline for declaring the significance according to computer simulation results.

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38

Jiang, Wun-Kai, and 江文楷. "A Simulation study of Poisson and Distributed Lag Models Under the structure of Zero-inflated outcomes." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/24980363292557310103.

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碩士
國立陽明大學
公共衛生研究所
104
For the count data, a popular method in medicine, public health and epidemiology research is the Poisson Regression Model estimating the relative risk (RR); Polynomial Distributed Lag Model (PDLM) is also a popular strategy in environmental epidemiology research to examine how the delayed environmental factors influence infectious disease. This model assumes that the dependent variable (Yt) is not only effected by the current independent variable (Xt), but also the lagged predictors (Xt-1, Xt-2 ...); Recently Distributed Lag Non-linear Model (DLNM) has been widely adapted. The feature of this model is that- it not only considers the lag effect between the environmental factor and the infectious disease, but also takes into account the non-linear relationship. However the performance of the three models for the zero-inflated outcome are unknown. Therefore, the simulation study we based on the time association between temperature and dengue fever under various models such as Zero-inflated Poisson, Zero-inflated Negative Binomial and Normal Distribution to estimate power of these three models in difference parameter settings. We will also implement using the permutation method under null hypothesis to evaluate the type I error rate performance for Polynomial Distributed Lag Model and Distributed Lag Non-linear Model. Finally, we applied the three models to the longitudinal data from 1998 to 2008 in Kaohsiung, Taiwan to confirm the association between dengue fever and temperature.

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Chang, Shu-Hao, and 張書豪. "Using Distributed lag non-linear models evaluates impact of AQI on cardiovascular diseases in the Taipei Basin." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/f9sr7k.

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碩士
國立陽明大學
生物醫學資訊研究所
103
Aim: Because of the rapid development of global industrialization and urbanization. It can cause serious adverse effects of air pollution. Many countries had developed air quality index (AQI) to protect the health of the population. This study estimated air pollutants regulatory standards in Taiwan, and analyzed air pollution data and health care data to explore association with air pollutants and cardiovascular disease (CVD) in the Taipei Basin. Materials and Methods: This study used K-means clustering estimate air pollutants regulatory standards and emission threshold in Taiwan, and explored the lag effects of air pollution on daily outpatients of CVD using Distributed Lag Non-linear Model (DLNM). Air pollutants and meteorological data from 16 air quality monitoring stations of the Environmental Protection Agency (EPA) in the Taipei Basin. Disease data from National Health Insurance Research Database. Disease diagnosis according The International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9 CM), including hypertension (401-405), ischemic heart disease (410-414), pulmonary circulatory diseases (415-417), other forms of heart disease (420-429) and cerebrovascular disease (430-438). Result: This study found CVD patients aged 41-64 years and female had strong association with air pollutions. In the high pollution event (AQI > 100), PM2.5 and CO had significant association with CVD, Relative risk (RR) was 1.077 (95% CI: 1.001 - 1.158) and 1.128 (95% CI: 1.014 - 1.255), respectively. In the low pollution event (AQI < 100), PM10 and NO2 had significant positive correlation, RR was 1.105 (95% CI: 1.032 - 1.184) and 1.13 (95% CI: 1.066 - 1.198), respectively. Effects of PM10, PM2.5 and NO2 at Lag0 and Lag1 is greatest on CVD outpatients. The result of K-means found the PM10 emission standard was 50.03 μg/m3 and PM2.5 emission standard was 28.9 μg/m3. Conclusion: This study found PM10, PM2.5, CO and NO2 had significant positive correlation with CVD outpatients in different AQI events. The results of K-mean can provide references of development of air quality index for EPA in the future. We can further explore the relationship between the chemical composition of air pollution and disease, and understand emission sources of particulate matter. And then we can have efficiently control of air pollution emissions.

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You, Zhen-Wei, and 游鎮瑋. "Power Comparisons of Polynomial Distributed Lag Models and Poisson Regressions for Associations between Temperature and Mortality with an Application in Taiwan." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/r77m8a.

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碩士
國立陽明大學
公共衛生研究所
103
Polynomial Distributed Lag Model (PDLM) is a popular method in environmental epidemiology research to examine how the environmental factors influence human’s health. This model assumes that the dependent variable (Yt) is not only effected by the current independent variable (Xt), but also the lagged predictors (Xt-1, Xt-2, ...). For the count data, another popular method in medicine, public health and epidemiology research is the Poisson Regression Model estimating the relative risk (RR) The aim of this thesis is to find out the best model to describe the effect of environmental factors on human’s health using the permutation method under alternative hypothesis to estimate power of these two models in difference parameter settings. We simulated the association between temperature and mortality under various models such as Poisson and normal for 1000 repetitions based on the longitudinal data from 1994 to 2008 in Taipei, Taiwan. The simulation results suggest that Poisson Regression Model has a better performance than the PDLM even with the most stringent Bonferroni’s correction. We also applied the both models to the longitudinal data in Taiwan and confirmed the association between mortality and temperature after controlling for the year, month, holiday, humidity, and ozone.

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41

Dzikiti, Weston. "Banking sector, stock market development and economic growth in Zimbabwe : a multivariate causality framework." Diss., 2017. http://hdl.handle.net/10500/22818.

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The thesis examined the comprehensive causal relationship between the banking sector, stock market development and economic growth in a multi-variate framework using Zimbabwean time series data from 1988 to 2015. Three banking sector development proxies (total financial sector credit, banking credit to private sector and broad money M3) and three stock market development proxies (stock market capitalization, value traded and turnover ratio) were employed to estimate both long and short run relationships between banking sector, stock market and economic growth in Zimbabwe. The study employs the vector error correction model (VECM) as the main estimation technique and the autoregressive distributed lag (ARDL) approach as a robustness testing technique. Results showed that in Zimbabwe a significant causal relationship from banking sector and stock market development to economic growth exists in the long run without any feedback effects. In the short run, however, a negative yet statistically significant causal relationship runs from economic growth to banking sector and stock market development in Zimbabwe. The study further concludes that there is a unidirectional causal relationship running from stock market development to banking sector development in Zimbabwe in both short and long run periods. Nonetheless this relationship between banking sector and stock markets has been found to be more significant in the short run than in the long run. The thesis adopts the complementary view and recommends for the spontaneity implementation of monetary policies as the economy grows. Monetary authorities should thus formulate policies to promote both banks and stock markets with corresponding growth in Zimbabwe’s economy.
Business Management
M. Com. (Business Management)

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