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Relevant bibliographies by topics / Distributed-lag model

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Academic literature on the topic 'Distributed-lag model'

Author: Grafiati

Published: 4 June 2021

Last updated: 24 January 2023

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Journal articles on the topic "Distributed-lag model"

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Ruiz Estrada, Mario Arturo, Evangelos Koutronas, and Ross Knippenberg. "The Mega Distributed Lag Model." Contemporary Economics 10, no. 2 (2016): 113–22. http://dx.doi.org/10.5709/ce.1897-9254.203.

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Tsay, Ruey S. "Model Identification in Dynamic Regression (Distributed Lag) Models." Journal of Business & Economic Statistics 3, no. 3 (1985): 228. http://dx.doi.org/10.2307/1391593.

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Tsay, Ruey S. "Model Identification in Dynamic Regression (Distributed Lag) Models." Journal of Business & Economic Statistics 3, no. 3 (1985): 228–37. http://dx.doi.org/10.1080/07350015.1985.10509454.

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Swanepoel, Ezelda. "Auto-regressive Distributed Lag Model for long-run US household debt determinants." Investment Management and Financial Innovations 16, no. 3 (2019): 40–48. http://dx.doi.org/10.21511/imfi.16(3).2019.05.

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US household debt increased on a yearly basis from 1987 to 2007. In addition, household debt in the USA nearly doubled between 2000 and 2007, from $5.6 trillion to $9 trillion. This came to an abrupt end in 2009 with the crash of the financial market. This paper employs the bound test and Auto-regressive Distributed Lag Model to determine the long-run relationship between US household debt and consumer prices, housing prices, the unemployment rate, and the lending rate. Unit root tests were conducted first to ascertain the stationarity of the variables. E-views 11 was used in the analysis of the data, which was obtained from Q1: 1990 to Q1: 2007 from the International Monetary Fund and the US FED. It was found that in the long run, there is a negative effect of consumer prices and unemployment on US household debt, while house prices and the lending rate would have a positive effect on household debt.

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LIHAWA, SRIRAPI H., RESMAWAN RESMAWAN, DEWI RAHMAWATY ISA, and LA ODE NASHAR. "DISTRIBUTED LAG MODEL PENGARUH JUMLAH UANG BEREDAR TERHADAP NILAI TUKAR RUPIAH MENGGUNAKAN METODE KOYCK DAN ALMON." Jambura Journal of Probability and Statistics 3, no. 1 (2022): 39–45. http://dx.doi.org/10.34312/jjps.v3i1.11805.

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A regression model that contains the dependent variable which is influenced by the current independent variable, and is also influenced by the independent variable at the previous time is called a distributed lag model. Distributed lag model is a dynamic model in econometrics that is useful in empirical econometrics because it makes a static economic theory dynamic by taking into account the role of time explicitly. There are two distributed lag models, namely the infinite lag model and the finite lag model using the Koyck method and the Almon method in determining the estimated Distributed lag model. This study aims to determine the Distributed lag model for the effect of the money supply on the rupiah exchange rate and determine the best model based on the Koyck method and the Almon method. From the results of selecting the best model based on the SIC value and judging by the more precise R2 of the Koyck method, the resulting model ist = 7958 + 0.0002Xt + 0.000177Xt-1+ 0.000157Xt-2+ 0.000139Xt-3 + 0.0000123Xt-4

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Lukman, Adewale F., and Golam B. M. Kibria. "Almon-KL estimator for the distributed lag model." Arab Journal of Basic and Applied Sciences 28, no. 1 (2021): 406–12. http://dx.doi.org/10.1080/25765299.2021.1989160.

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Tarasov, Vasily E., and Valentina V. Tarasova. "Harrod–Domar Growth Model with Memory and Distributed Lag." Axioms 8, no. 1 (2019): 9. http://dx.doi.org/10.3390/axioms8010009.

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In this paper, we propose a macroeconomic growth model, in which we take into account memory with power-law fading and gamma distributed lag. This model is a generalization of the standard Harrod–Domar growth model. Fractional differential equations of this generalized model with memory and lag are suggested. For these equations, we obtain solutions, which describe the macroeconomic growth of national income with fading memory and distributed time-delay. The asymptotic behavior of these solutions is described.

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Lopo, Alexandre Boleira, Maria Helena Constantino Spyrides, Paulo Sérgio Lucio, and Javier Sigró. "UV Index Modeling by Autoregressive Distributed Lag (ADL Model)." Atmospheric and Climate Sciences 04, no. 02 (2014): 323–33. http://dx.doi.org/10.4236/acs.2014.42033.

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Hu, Wei, and Norman M. Wereley. "Distributed Rate-Dependent Elastoslide Model for Elastomeric Lag Dampers." Journal of Aircraft 44, no. 6 (2007): 1972–84. http://dx.doi.org/10.2514/1.26409.

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Virgantari, Fitria, and Wilda Rahayu. "PENDUGAAN PARAMETER MODEl DISTRIBUTED LAG POLA POLINOMIAL MENGGUNAKAN METODE ALMON." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 4 (2021): 761–72. http://dx.doi.org/10.30598/barekengvol15iss4pp761-772.

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The distributed lag model is a regression model that describes the relationship between the dependent variable of a given period and the independent variables of a certain or previous periods. The model can be used to determine the impact of the independent variable to dependent variables over time and forecast time series data for the next periods. There are two forms of distributed lag model that have been widely proposed in the estimation of distributed lag regression model. The first form is proposed by Koyck and the second form by Almon. This paper aims to apply the Almon model to examine the effect of the ratio of BOPO (Operating Cost and Operating Income) to the ROA (Return on Asset) of a government bank based on quarterly data, to estimate its parameters, to examine the feasibility of the model, and to predict the next quarter. Results shows that distributed lag model is = 10.110 - 0.078 + 0.015 + 0.026 – 0.045 with Yt is ROA, and Xt is the ratio BOPO on the 1st quarter until the previous 3 quarters. The model is quite good according to the determination coefficient that is 0.75, no autocorrelation in the model, t test and F test are also significant. Based on the model, the value of ROA ratio next quarter predicted 4.63%. The decrease in profitability ROA ratio is due to an increase in interest expense while interest income can not compensate

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Dissertations / Theses on the topic "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.<br>MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.<br>Bibliography: leaves 204-216.<br>by Samuel Mintz.<br>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<br>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.<br>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.<br>Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

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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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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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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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Book chapters on the topic "Distributed-lag model"

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Liu, Anyu, and Xinyang Liu. "The autoregressive distributed lag model." In Econometric Modelling and Forecasting of Tourism Demand. Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-3.

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Gupta, Ritu, Madhu Jain, and Anuradha Jain. "Software Reliability Growth Model in Distributed Environment Subject to Debugging Time Lag." In Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0857-4_7.

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Rahmouni, Abdelwahab, Mohamed Meddi, and Hafsa Karahaçane. "Modeling and Forcasting of Surface Runoff in the Beni Bahdel Dam: Using ARDL Model (Autoregressive Distributed Lag)." In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70548-4_241.

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Ha, Nguyen Thi Vinh. "Evaluating Impact of Climate Change to Fishing Productivity of Vietnam: An Application of Autoregressive Distributed Lag (ARDL) Regression Model." In Global Changes and Sustainable Development in Asian Emerging Market Economies Vol. 2. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81443-4_43.

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Knottnerus, Paul. "Distributed Lag Models and Correlated Disturbances." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-48383-7_6.

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Magrini, Alessandro. "The impact of public research expenditure on agricultural productivity: evidence from developed European countries." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.12.

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The objective of this paper is to assess the impact of public research expenditure on agricultural productivity in developed European countries. Our research provides original evidence, making possible a comparison with existing studies focused on United States of America (USA). We apply a fixed effects Gamma distributed-lag model to yearly data in 1970-2016 sourced from the United States Department of Agriculture (USDA), the Organisation for Economic Cooperation and Development (OECD), and the Food and Agriculture Organization (FAO). In our results, public research expenditure has a significant impact on agricultural productivity up to 35 years, with peak at 17 years and long-term elasticity equal to 0.172. Based on our model, the countries with the highest internal rate of return of agricultural research expenditure resulted Germany, Spain, France and Italy (24.5-25.2%), followed by Netherlands, United Kingdom, Denmark, Greece, Belgium and Luxembourg (20.5-21.8%). However, only Germany, Denmark and Greece increased agricultural research expenditure in recent years. The estimated internal rates of return are in line with the ones reported by existing studies on USA, and they suggest that developed European countries, just like USA, could benefit from research investments in Agriculture to a much greater extent than they currently do.

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Udelhoven, Thomas, Marion Stellmes, and Achim Röder. "Assessing Rainfall-EVI Relationships in the Okavango Catchment Employing MODIS Time Series Data and Distributed Lag Models." In Remote Sensing Time Series. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15967-6_11.

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"23 Phillips model with distributed lag and memory." In Economic Dynamics with Memory. De Gruyter, 2021. http://dx.doi.org/10.1515/9783110627459-023.

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"21 Harrod–Domar model with memory and distributed lag." In Economic Dynamics with Memory. De Gruyter, 2021. http://dx.doi.org/10.1515/9783110627459-021.

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Wen, Jiatao, Cheng Ji, Jingde Wang, and Wei Sun. "Autoregressive Distributed Lag Model Based Cointegration Analysis for Batch Process Monitoring." In Computer Aided Chemical Engineering. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-85159-6.50240-2.

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Conference papers on the topic "Distributed-lag model"

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Conrad, Jessica, Amanda Ziemann, Randall Refeld, et al. "Understanding polynomial distributed lag models: truncation lag implications for a mosquito-borne disease risk model in Brazil." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2019. http://dx.doi.org/10.1117/12.2536369.

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Wang, Jiasheng. "Forecast GDP with Autoregressive Distributed Lag Model and Dynamic Factor Model." In ICCIR 2021: 2021 International Conference on Control and Intelligent Robotics. ACM, 2021. http://dx.doi.org/10.1145/3473714.3473783.

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Hamid, Mohd Fahmi Abdul, and Ani Shabri. "Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach." In THE 3RD ISM INTERNATIONAL STATISTICAL CONFERENCE 2016 (ISM-III): Bringing Professionalism and Prestige in Statistics. Author(s), 2017. http://dx.doi.org/10.1063/1.4982864.

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MEHTA, JAYESH, P. MUNGUR, W. DODDS, and L. DODGE. "A distributed vaporization time-lag model for gas turbine combustor dynamics." In 28th Joint Propulsion Conference and Exhibit. American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-3465.

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Huda, Nur'ainul Miftahul, Utriweni Mukhaiyar, and Udjianna Sekteria Pasaribu. "Forecasting dengue fever cases using autoregressive distributed lag model with outlier factor." In THE 4TH INDOMS INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATION (IICMA 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0018450.

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Miao, Enming, Pengcheng Niu, Yetai Fei, and Yan Yan. "Application of autoregressive distributed lag model to thermal error compensation of machine tools." In Seventh International Symposium on Precision Engineering Measurements and Instrumentation, edited by Kuang-Chao Fan, Man Song, and Rong-Sheng Lu. SPIE, 2011. http://dx.doi.org/10.1117/12.905451.

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Fitri, Fadhilah, Toni Toharudin, and I. Gede Nyoman Mindra Jaya. "Marine capture fisheries production and intensity of rainfall: An application of autoregressive distributed lag (ARDL) model." In STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4979454.

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Xiaoyu Wan, Zhuxuanzi Deng, and Zifu Fan. "Study on the relationship between fixed assets investment and business income of the telecommunication enterprises based on distributed lag model." In 2010 International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2010. http://dx.doi.org/10.1109/fitme.2010.5654915.

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Jin, Renshu, and Rendong Han. "The comparative study of the effects of FDI from US and Japan on China: Empirical study based on distributed lag model." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5882044.

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Nguyen, Mien T. N., and Man V. M. Nguyen. "Application of Thin-plate Spline and Distributed Lag Non-linear Model to Describe the Interactive Effect of Two Predictors on Count Outcomes." In 2022 9th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2022. http://dx.doi.org/10.1109/nics56915.2022.10013444.

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