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Brüggemann, Ralf. "Model reduction methods for vector autoregressive processes /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0818/2003067373-d.html.
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Schnücker, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.
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Camehl, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.
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Horton, Wendy Elizabeth. "A vector autoregressive model of a regional Phillips curve in the United States." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30515.
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Lee, Joo Young, and Youn Mi Lee. "Dynamic Impact of Aging on Income Inequality in the U.S. with Vector Autoregressive Model." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/secfr-conf/2020/schedule/57.
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Income inequality has been showing a steady increase for past decades and will be worsened in the future (Piketty, 2014). One of the most important factors to explain the worsening income inequality can be aging. Previous studies on aging focus on its impact on traditional issues such as health, retirement, and economic growth. This study finds the direct relationship between aging and income inequality using the vector autoregressive (VAR) model (Blanchard and Quah, 1989). The VAR model is useful to analyze the long-run response of aging on income inequality. The empirical results will verify the negative impact of aging on income inequality in the U.S. The governmental efforts to reduce the negative impact of aging on health care and pensions could delay the worsening income inequality.
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Serpeka, Rokas. "Analyzing and modelling exchange rate data using VAR framework." Thesis, KTH, Matematik (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-94180.
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Abstract In this report analysis of foreign exchange rates time series are performed. First, triangular arbitrage is detected and eliminated from data series using linear algebra tools. Then Vector Autoregressive processes are calibrated and used to replicate dynamics of exchange rates as well as to forecast time series. Finally, optimal portfolio of currencies with minimal Expected Shortfall is formed using one time period ahead forecasts
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Crespo, Cuaresma Jesus, Gernot Doppelhofer, Martin Feldkircher, and Florian Huber. "Spillovers from US monetary policy: Evidence from a time-varying parameter global vector autoregressive model." Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society, 2019. http://dx.doi.org/10.1111/rssa.12439.
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The paper develops a global vector auto-regressive model with time varying pa-
rameters and stochastic volatility to analyse whether international spillovers of US monetary
policy have changed over time. The model proposed enables us to assess whether coefficients
evolve gradually over time or are better characterized by infrequent, but large, breaks. Our find-
ings point towards pronounced changes in the international transmission of US monetary policy
throughout the sample period, especially so for the reaction of international output, equity prices
and exchange rates against the US dollar. In general, the strength of spillovers has weakened
in the aftermath of the global financial crisis. Using simple panel regressions, we link the vari-
ation in international responses to measures of trade and financial globalization. We find that
a broad trade base and a high degree of financial integration with the world economy tend to
cushion risks stemming from a foreign shock such as US tightening of monetary policy, whereas
a reduction in trade barriers and/or a liberalization of the capital account increase these risks.
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Degerli, Mecit Mert. "Fault detection of bearings based on AutoRegressive modelling and Support Vector Machine classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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Bearings are the common mechanical components and they have an authentic role generally in every kind of machines or applications. Therefore it becomes more and more important to understand properly the behaviour and the nature of the bearing and also their working conditions. For this reason, the data from the components on the machine as bearings in the modern industry; the importance of reducing machine downtime and machine costs, increasing machine performance and achieving a more stable operation is increasing day by day. For that purpose, different solutions have been provided to detect and diagnose faulty situations in bearings with different application-oriented approaches. In this study, by means of vibration based method; the vibration signals coming from the bearings are filtered by Wavelet analysis to avoid non-stationarities and then the filtered signal is modeled according to the AutoRegressive (AR) process. The coefficients of the related model is the basis for the Machine Learning algorithm Support Vector Machines (SVM) by optimizing with Bayesian Optimization to perform Fault Detection and Identification (FDI). This usage of AR process provides a useful forward-looking method for appropriate feature selection. In this study the data which was provided by Center for Intelligent Maintenance Systems is used and the results have been discussed and concluded.
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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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Dashti, Hossein, Antonio J. Conejo, Ruiwei Jiang, and Jianhui Wang. "Weekly Two-Stage Robust Generation Scheduling for Hydrothermal Power Systems." IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016. http://hdl.handle.net/10150/622668.
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As compared to short-term forecasting (e.g., 1 day), it is often challenging to accurately forecast the volume of precipitation in a medium-term horizon (e.g., 1 week). As a result, fluctuations in water inflow can trigger generation shortage and electricity price spikes in a power system with major or predominant hydro resources. In this paper, we study a two-stage robust scheduling approach for a hydrothermal power system. We consider water inflow uncertainty and employ a vector autoregressive (VAR) model to represent its seasonality and accordingly construct an uncertainty set in the robust optimization approach. We design a Benders' decomposition algorithm to solve this problem. Results are presented for the proposed approach on a real-world case study.
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Gkolitsis, Petros. "Interactions of monetary policies in South East Europe in a European Monetary Union context : a global-vector autoregressive model." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/21736/.
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This thesis discusses the interactions of monetary policies in the South-eastern European Countries (SEEC) in a European Monetary Union (EMU) context, by modelling via a Global Vector Autoregressive Model (G-VAR) the interdependencies arising between the member states and the related financial institutions in the region. The EMU and its relation to monetary or economic policy interactions has been heavily and effectively researched by numerous researchers including, indicatively, the Nobel laureate Mundell (1961) who theoretically and empirically considered and examined the effects of monetary and fiscal policies coordination on real output, interest rates and exchange rates with the aim of increasing the benefits that could arise from an optimum currency area (OCA). A G-VAR model for South-Eastern Europe (SEE), however, has not been applied and foreign exchange reserves have not yet been considered within such a contextual framework. There is a gap to fill in on the theoretical and empirical relation of the aforementioned variables using econometrics and we will do so by using a multi-simultaneous equations system with weak exogeneity, i.e. a G-VAR. The incorporated variables are: the foreign exchange reserves, the real effective exchange rate (REER), the growth approximated by the industrial production index (IPI) and the monetary policy which is quantified through interest rates and specifically by the money and market rate. The variables that will be treated as weakly exogenous within the GVAR system are the Euribor and the EMU Real Effective Exchange Rate. The frequency of the data is monthly and covers the period from 2002 to 2016. The analysis is conducted with the use of secondary data which is acquired through publicly available published data and reports from Central Banks, the European Central Bank (ECB), Eurostat, OECD, BIS, IMF and the World Bank. The European Countries that are considered are Bulgaria, Croatia, FYROM, Greece, Romania and Slovenia. The European Monetary Union and its role are captured by the related interest rate, i.e. Euribor, and the Real Effective Exchange rates of EMU members as a whole. The main task of the project is to capture the transmission mechanism −from the monetary to real economy− by considering the role of foreign exchange reserves in the case of SEE countries. This adds to the understanding of the economic policy effect on nominal and real variables, suggests a better macroeconomic policy design and adds to the efficiency of the implementation of monetary policy that captures complexities that are related to an Optimum Currency Area (OCA). On top of the above the EMU REER helps us in understanding the existing and dynamically changing competitive related interlinkages that exist between the investigated variables.
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Demeš, Jiří. "Ekonometrická analýza vývoje inflace v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-4847.
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The degree work is focused on analysis of inflation with help of suitable econometric models. Inflation with it's forms and possibilities of measuring is described at the beginning of the paper. There is mentioned an importance of monitoring and analysing inflation in view of Czech national bank. Consequently there are described characteristics of time series, which are important from viewpoint of construction of econometric models. Next part of this paper is focused on characterization of econometrics models. At first there is vector autoregression model, in this connection there is discussed the essence of Granger causality and impulse reaction. There are also noticed both error correction model and vector error correction model. The empirical part of degree work involves the use of these models on selected macroeconomic time series of the Czech republic. The objective is to analyze the relationship between inflation and other individual macroeconomic quantities. There is established the optimal vector autoregressive model and the results of Granger causality and impulse reaction are interpretated. Both error correction model and vector error correction model examining cointegration are also applied.
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Oliveira, Jorge Manuel Caetano de. "Significado do 1º pilar da política monetária do Eurosistema." Master's thesis, Instituto Superior de Economia e Gestão, 2002. http://hdl.handle.net/10400.5/3582.
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Mestrado em Economia Monetária e FinanceiraO presente estudo tem como objectivo fundamental investigar qual o significado do papel atribuído pelo Banco Central Europeu (BCE) ao agregado monetário largo M3 na condução da política monetária na zona euro. Partindo de um breve enquadramento teórico quanto ao papel atribuído pelo BCE à moeda, procede-se a uma análise empírica, baseada em vectores autoregressivos (usando essencialmente as variáveis macroeconómicas: taxa de crescimento do M3, hiato do M3 em termos reais, taxa de inflação e hiato do produto), no sentido de detectar evidência empírica de suporte ao papel proeminente que foi atribuído à moeda pelo BCE. As principais conclusões deste estudo apontam para a existência de uma enorme sensibilidade em torno da definição da taxa de inflação. No entanto, partindo-se da taxa de inflação medida pelo deflator do Produto Interno Bruto (PIB), a evidência empírica parece sugerir uma certa primazia como indicador de evolução futura dos preços o hiato do M3 em termos reais quando comparado quer com a taxa de crescimento do agregado monetário largo M3 quer com o hiato do produto.
The main target of this study is to investigate the meaning of the role assigned by the European Central Bank (ECB) to the broad monetary aggregate M3 in its conduction of the monetary policy for the euro area. The work begins with a brief theoretic framing of the role assigned to the money by ECB, which is followed by an empirical analysis, based on autoregressive vectors (employing essentially the following macroeconomic variables: growth rate of M3, real gap of M3, inflation rate and output gap), with the objective of detecting empirical evidence that support the prominent role of money assigned by the ECB. The main conclusions of this study support the existence of a high sensibility to the definition of the inflation rate. However, starting with the best measure of inflation rate based on Gross Domestic Product (GDP) deflator, the empirical evidence supports a certain primacy, as future inflation indicator, to the real gap of M3 rather than to the growth rate of M3 or output gap.
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Jansson, Emelie, and Linda Kapple. "Vad styr företagens investeringar?En studie om hur förändringar i reporänta, makroekonomiska faktorer samt finansiella indikatorer påverkar investeringar hos svenska företag." Thesis, Linköpings universitet, Nationalekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121716.
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Bakgrund: I november 2014 beslutade Riksbanken att ta steget mot en nollränta och i februari 2015 gick Riksbanken ut med ytterligare en sänkning till -0,10 procent. På så vis fick Sverige för första gången en negativ reporänta. Enligt makroekonomisk teori ska en sänkning av reporäntan stimulera konsumtion och investeringar i ekonomin. Huruvida reporäntan och dess räntesänkningar skapar förutsättningar för företag att investera är ett aktuellt och viktigt forskningsområde. Forskningen i ämnet är tunn på den svenska marknaden och således är forskningsbidraget från denna studie av betydelse.Syfte: Syftet med studien är att undersöka och analysera hur förändringar i reporänta, makro-ekonomiska faktorer samt finansiella indikatorer påverkar investeringar hos svenska företag.Genomförande: Studien bygger på en kvantitativ metod. En Vector Autoregressive model har skapats för att redogöra hur reporäntan, de makroekonomiska faktorerna och de finansiella indikatorerna påverkar företagens investeringar. För att möjliggöra en analys av dessa effekter har impulse response functions skattats i modellen. På så vis undersöks det hur en isolerad enhetsökning i de valda variablerna påverkar företagens investeringar över flera tidsperioder. För att genomföra en mer omfattande analys skattas tre modeller där den första tar hänsyn till både makroekonomiska faktorer och finansiella indikatorer. Den andra modellen exkluderar de finansiella indikatorerna och den tredje modellen speglar reporäntans utveckling i två olika tidsperioder.Resultat: Företagens investeringar påverkas av flertalet faktorer. En enhetsökning av utlåningsräntan, växelkursen och företagens inflationsförväntningar uppvisar ett signifikant negativt samband. En enhetsökning av BNP-tillväxten visar däremot ett signifikant positivt samband. Reporäntan visar ingen direkt effekt på investeringar i de första två modellerna. Däremot uppvisar reporäntan skillnader i den tredje modellen, där ett negativt samband förekommer i den första av de två observerade tidsperioderna.Background: The central bank of Sweden decided in November 2014 to set the repo rate close to zero. Further they decided to lower the repo rate to -0,10 percent in February 2015. In regard to this, Sweden had a negative repo rate for the first time. According to macroeconomic theory a decrease in the repo rate is performed to stimulate an economy’s investments and consumptions. Whether or not a decrease in interest rates gives greater incentives for firms to invest is a topical subject and an important field of research. In addition to this, the existing research on the Swedish market is insufficient within this field, which gives us further motives to conduct this study.Aim: The purpose of this study is to examine and analyse how changes in the repo rate, macroeconomic factors and financial indicators affects investments of Swedish firms.Completion: The study is conducted with a quantitative approach. A Vector Autoregressive model is created in order to examine the impact of changes in the repo rate, the macroeconomic factors and the financial indicators on firms’ investments. Impulse response functions are estimated to allow a further analysis of these effects. Hence, it is conceivable to examine how one isolated unit-increase in a specific variable affects firms’ investment through several time periods. Furthermore, we estimate three models, one which includes both macroeconomic variables and financial indicators and another which excludes the financial indicators. The last model reflects the repo rate’s impact on investments in two separate time periods.Result: Investments of firms are affected by numerous of factors. One unit-increase of the lending rate, the exchange rate and firms’ expectations of inflation exhibit a negative relation to investments. Furthermore, one unit-increase in GDP-growth tends to increase investments. However, the repo rate has no impact on investments in the first two models. In spite of this, evidence from the third model indicates that the repo rate has a negative impact on investments during the first period.
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Ribeiro, Maria Josà Pontes. "A study on the impact of fiscal policy in the collection of VAT in the state of Ceara by vector autoregressive model." Universidade Federal do CearÃ, 2010. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=4777.
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nÃo hÃThis research aims to analyze quantitatively impacts of tax policy promoted by the Federal Government on the behavior of tax revenues of ICMS in CearÃ. Thus, we considered information on the tax revenues of industrial, trade and electric, beyond the rate of open unemployment in Fortaleza and how the transmission mechanism of fiscal policy used the ratio between expenditure and revenue of the Federal Government. The model used here is composed of vector autoregression and arguments based on Toda and Yamamoto (1995), and impulse response functions and variance decomposition. The results suggest that a positive shock on fiscal policy has no impact on the sector of tax revenues of ICMS by SEFAZ-CE. However, shocks in the tax revenue of industry exert positive impact on the unemployment rate in CearÃ.
Esta pesquisa se propÃe a analisar de maneira quantitativa os impactos da polÃtica fiscal promovida pelo Governo Federal sobre o comportamento da arrecadaÃÃo setorial de ICMS no CearÃ. Para isso, foram consideradas informaÃÃes sobre a arrecadaÃÃo dos setores industrial, comÃrcio varejista e elÃtrico, alÃm da taxa de desemprego aberta de Fortaleza e como mecanismo de transmissÃo da polÃtica fiscal utilizou-se a razÃo entre as despesas e as receitas do Governo Federal. O modelo empregado aqui à composto por vetores autorregressivos e baseados nos argumentos de Toda e Yamamoto (1995), alÃm de funÃÃes impulso resposta e decomposiÃÃo da variÃncia. Os resultados sugerem que um choque positivo na polÃtica fiscal nÃo exerce impacto sobre as arrecadaÃÃes setoriais de ICMS pela SEFAZ-CE. PorÃm, choques na arrecadaÃÃo setorial exercem impacto positivo sobre a taxa de desemprego cearense.
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Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.
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Master of ScienceDepartment of Statistics
Juan Du
Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.
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Jia, Mo (Maggie). "Housing market, banking sector and macroeconomy in China." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/279056.
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This thesis contains three main parts. In the first part, we adapt a model developed for the US economy to the unique Chinese economic and institutional context. The uniqueness is mainly from two perspectives: the dual-channel housing financing system in China and the existence of the shadow banking sector (which differs from the shadow banking in developed economies) in China’s housing market. It would be difficult to obtain a clear picture of the Chinese housing market and macroeconomy without a thorough understanding of these two characteristics. This is due to the crucial role played by shadow banking and other informal finance institutions within the context of China in both the development and purchase of housing, in supporting productive economic activities in general, and that the housing market is in turn intricately connected to the health of the Chinese economy, being a key ‘barometer’. The second part of the research is the quantification of the determinants of the scale of shadow banking in China. The quantification is crucial since policy makers need to be aware of how sensitive shadow banking is to various factors. We develop a theoretical framework to explain the evolution of the scale of shadow banking in China. As part of this research, we investigate whether the real interest rate of household saving deposits, the required reserve ratio and bank loans to business and household are the main factors in explaining the evolution of China’s shadow banking. In the third part of research, we employ a credit risk and macroeconomic stress test to investigate the vulnerability of the commercial banks in China. Our originality here is the integration of both the role of shadow banking and housing market related loans in the commercial banks’ stress test scenarios at the macro level. Since a systematic analysis regarding the effect of changes in the macroeconomy and housing market on the credit risk of commercial banks in China is scarce, we use bank stress tests to analyse the credit risk in terms of the non-performing loans ratio of commercial banks in China; this is in response to changes in the macroeconomic factors and housing market. We address the role of the variation of the scale of shadow banking in China in terms of its contribution to the credit risk because of its uncertainty and close link with the commercial banks. Stress tests often focus on a single bank or financial institution yet we apply the same principles to examine the financial system as a whole in China, which would allow us to quantify the systemic risk in the entire Chinese financial system; and which variables, especially shadow banking contribute to the risks and by how much. This thesis contributes to the understanding of how China’s dual-channel housing finance system and shadow banking affect the evolution of house prices; and also, the main driving factors of the scale of China’s shadow banking and whether the housing market related loans and shadow banking pose risks to commercial banks. Possible research questions raised by the main findings of this thesis will enrich the debate on China’s housing market, shadow banking and regular banks, especially at a time when China is reforming its economic structure.
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Fischer, Manfred M., Florian Huber, and Michael Pfarrhofer. "The regional transmission of uncertainty shocks on income inequality in the United States." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/6774/1/2018%2D01%2D10_FischerHuberPfarrhofer_Inequality.pdf.
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This paper explores the relationship between household income inequality and macroeconomic
uncertainty in the United States. Using a novel large-scale macroeconometric
model, we shed light on regional disparities of inequality responses to a national uncertainty
shock. The results suggest that income inequality decreases in most states, with a
pronounced degree of heterogeneity in terms of the dynamic responses. By contrast,
some few states, mostly located in the Midwest, display increasing levels of income
inequality over time. Forecast error variance and historical decompositions highlight
the importance of uncertainty shocks in explaining income inequality in most regions
considered. Finally, we explain differences in the responses of income inequality by means
of a simple regression analysis. These regressions reveal that the income composition as
well as labor market fundamentals determine the directional pattern of the dynamic responses.Series: Working Papers in Regional Science
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Zhong, Jiansheng. "Essays on Small Open Economies." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491951614255482.
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Fischer, Manfred M., Florian Huber, and Michael Pfarrhofer. "The transmission of uncertainty shocks on income inequality: State-level evidence from the United States." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6368/1/us%2Dstates_uncertainty.pdf.
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In this paper, we explore the relationship between state-level household income inequality and macroeconomic
uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed
light on regional disparities of inequality responses to a national uncertainty shock. The results suggest
that income inequality decreases in most states, with a pronounced degree of heterogeneity in terms of
shapes and magnitudes of the dynamic responses. By contrast, some few states, mostly located in the
West and South census region, display increasing levels of income inequality over time. We find that
this directional pattern in responses is mainly driven by the income composition and labor market fundamentals.
In addition, forecast error variance decompositions allow for a quantitative assessment of
the importance of uncertainty shocks in explaining income inequality. The findings highlight that volatility
shocks account for a considerable fraction of forecast error variance for most states considered.
Finally, a regression-based analysis sheds light on the driving forces behind differences in state-specific
inequality responses.Series: Working Papers in Regional Science
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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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Fidalgo, Cristina Patrícia Gouveia Dias. "Teoria generalizada da paridade do poder de compra : uma aplicação às economias da Europa Central." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21111.
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Mestrado em Econometria Aplicada e PrevisãoNo presente estudo pretende-se analisar se os três países da Europa Central em vias de aderir à União Económica Monetária - Hungria, Polónia e República Checa - constituem, de facto, uma Zona Monetária Ótima no espírito de Mundell (1961) com os países da Zona Euro vis-à-vis a economia da Alemanha, colocando, assim, um fim à fase de transição dos últimos 16 anos. Para tal, recorre-se à teoria Generalizada da Paridade do Poder de Compra, inicialmente proposta por Enders e Hurn (1994), empiricamente testável com recurso ao modelo vetorial de correção de erros. Os resultados empíricos indicam que, para o período entre 1993 e 2019, apesar da não estacionaridade das séries (do logaritmo) da taxa de câmbio real bilateral de cada economia, existe, efetivamente, um co-movimento entre as diferentes taxas em trajetória de equilíbrio, refletindo o processo de convergência real consistente com o critério de Zona Monetária Ótima.
The present study aims to analyze whether the three Central European countries that are about to join the Economic Monetary Union - Hungary, Poland and the Czech Republic - constitute, indeed, an Optimum Currency Area in the spirit of Mundell (1961) with the countries of the Eurozone vis-à-vis the German economy, thus putting an end to the transition phase of the past 16 years. The theoretical framework is based on Generalized Theory of Purchasing Power Parity, an hypothesis initially proposed by Enders and Hurn (1994), empirically testable using the vector error correction model. The empirical results indicate that, for the period between 1993 and 2019, despite the non-stationarity of the series (of the logarithm) of the bilateral real exchange rate of each economy, there is, effectively, a co-movement between the different rates on an equilibrium path, reflecting the process of real convergence consistent with the Optimum Currency Area criterion.
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Chi, Nam Yau. "Economically justified equity investment strategies capable of withstanding growing interest rate environment." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/18823.
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Mestrado em Economia Monetária e FinanceiraThis thesis proposes an approach for selection of stocks that could serve as a natural hedge for fixed income portfolios to minimize rising interest rate risk. The developed approach is applied to the case of US equity markets. Based on macroeconomic analysis, vector autoregressive model and Granger causality tests, and financial analysis, it is concluded that US financial sector is the optimal choice among all sectors that have strong correlations with interest rates. The thesis? results could be useful for interest rate risk management of the investment portfolios under the growing interest rate environment, in particular, and for investment industry professionals.
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Hörnell, Fredrik, and Melina Hafelt. "Responsiveness of Swedish housing prices to the 2018 amortization requirement : An investigation using a structural Vector autoregressive model to estimate the impact of macro prudential regulation on the Swedish housing market." Thesis, Södertörns högskola, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-35533.
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This thesis analyzed and estimated the impact of the March 1, 2018 loan to income amortization requirement on residential real estate prices in Sweden. A four variables vector autoregressive model (VAR) was used to study the relationships between residential real estate prices, GDP, real mortgage rate and consumer price index over a time period from 2005 to 2017. First, a structural vector autoregressive (SVAR) model was used to test how a structural innovation in the error term for real mortgage rate affected residential real estate prices. Secondly, an unconditional forecast from our reduced VAR was produced to estimate post 2017 price growth of the Swedish housing market. The impulse response function results stand in contradiction to economic intuition i.e. the price puzzle problem. The unconditional forecast indicates that the housing market will enter a period with slower price growth post 2017, which are in line with previous research. This thesis vector autoregressive model can give meaningful results with regard to trend forecasts but with regard to precise statements as anticipating drastic price depreciation, it falls short. We recommend the use of reduced VAR forecasting with regard to the Swedish housing market.
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Akin, Serdar. "Do Riksbanken produce unbiased forecast of the inflation rate? : and can it be improved?" Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-58708.
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The focus of this paper is to evaluate if forecast produced by the Central Bank of Sweden (Riksbanken) for the 12 month change in the consumer price index is unbiased? Results shows that for shorter horizons (h < 12) the mean forecast error is unbiased but for longer horizons its negatively biased when inference is done by Maximum entropy bootstrap technique. Can the unbiasedness be improved by strict ap- pliance to econometric methodology? Forecasting with a linear univariate model (seasonal ARIMA) and a multivariate model Vector Error Correction model (VECM) shows that when controlling for the presence of structural breaks VECM outperforms both prediction produced Riksbanken and ARIMA. However Riksbanken had the best precision in their forecast, estimated as MSFE
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Colot, Olivier. "Apprentissage et détection automatique de changements de modèles : application aux signaux électroencéphalographiques." Rouen, 1993. http://www.theses.fr/1993ROUES012.
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La thèse présentée traite du problème de la détection de changements de modèles dans des signaux vectoriels lentement variables. L'étude s'articule autour de deux thèmes: modélisation vectorielle, détection de changements de modèles. Le premier thème est traité sous l'angle d'une technique récursive de modélisation linéaire, tirant profit des propriétés de stationnarité locale des signaux étudiés dans un contexte vectoriel. Dans une seconde partie, le problème de la détection de changements de modèles est étudié. Deux approches sont proposées et testées: la première s'appuie sur un critère d'énergie d'erreurs issues de la modélisation, la seconde est fondée sur la comparaison d'histogrammes approchant des lois de probabilité, les histogrammes étant construits à l'aide d'un critère de type Akaike. La détection de changements de modèles, synonymes de changements de lois, est effectuée à l'aide de mesure de dissemblance. La validation de ces méthodes est réalisée sur des signaux biomédicaux: les signaux électroencéphalographiques
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Rincón, Diana Constanza García. "Performane of partial directed coherence subject to volume consuction effects." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3154/tde-12072017-131157/.
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The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC.A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.
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Fischer, Manfred M., Florian Huber, Michael Pfarrhofer, and Petra Staufer-Steinnocher. "The dynamic impact of monetary policy on regional housing prices in the United States." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6658/1/2018%2D11%2D16_housing_favar_(002).pdf.
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This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary
policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously
estimate the model parameters and unobserved factors we rely on Bayesian estimation
and inference. Policy shocks are identified using high-frequency suprises around policy
announcements as an external instrument. Impulse reponse functions reveal differences in regional
housing price responses, which in some cases are substantial. The heterogeneity in policy
responses is found to be significantly related to local regulatory environments and housing supply
elasticities. Moreover, housing prices responses tend to be similar within states and adjacent
regions in neighboring states.Series: Working Papers in Regional Science
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Маринич, Тетяна Олександрівна, Татьяна Александровна Маринич, Tetiana Oleksandrivna Marynych, Людмила Дмитрівна Назаренко, Людмила Дмитриевна Назаренко, Liudmyla Dmytrivna Nazarenko, Надія Володимирівна Тиркусова, Надежда Владимировна Тыркусова, and Nadiia Volodymyrivna Tyrkusova. "Моделювання коінтеграційних процесів для удосконалення механізму монетарної трансмісії." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/68086.
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Монетарний трансмісійний механізм – процес передачі
змін у використанні інструментів монетарної політики
центрального банку на фінансовий сектор економіки, а у
подальшому – на макроекономічні змінні на основі
використання певних каналів і зв’язків прямої та зворотної
дії. Для його удосконалення необхідно виявлення терміну
дії зв’язків між визначеними макроекономічними
змінними та показниками грошово-кредитної та валютної
політики. Він може бути як коротко так і довгостроковим.
Дослідження часових рядів, представлених квартальними
статистичними даними Національного банку України за
2002-2011 рр., дозволило побудувати векторну авто
регресійну модель корекції помилки (VECM). За нею
можна констатувати наявність як короткострокових так і
довгострокових зв’язків між певними макроекономічними
показниками.
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Sugita, Katsuhiro. "Bayesian analysis of cointegrated vector autoregressive models." Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/66201/.
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This thesis concerns econometric time series modelling of cointegrated multivariate systems using a Bayesian approach. The Bayesian approach has become increasingly attractive among researchers in the fields such as biology, though still only a relatively few econometricians use these techniques. Rather than theoretical aspects of Bayesian statistics or computational techniques, we illustrate how the Bayesian methods can be useful in analysing non-linear cointegration models. In the last ten years, non-linear time series models, such as regime switching models, have become popular among applied econometricians to analyse the business cycles, policy evaluation in specific macroeconomic issues and forecasting. Cointegration analysis has been influenced by the non-linearity so that cointegration models that allow regime switching or structural breaks have been analysed by many econometricians. Unfortunately, these nonlinear cointegration models tend to be complicated both in terms of estimation and testing. We consider in this thesis a Bayesian approach to (i) a linear cointegration model, (ii) a cointegration model with Markov regime switching, and (iii) a cointegration model with multiple structural breaks, and show how easily we can analyse these models without any substantial modification. Chapter 2 proposes a simple method for detecting cointegration rank using the Bayese factors, computed by the harmonic mean of the likelihood or Schwarz' Bayesian information criterion. Then we perform Monte Carlo simulations to compare three Bayesian methods (Phillips posterior information criterion, Kleibergen and Paap method, and one proposed method) for the cointegration rank. Provided we have enough large sample size, the Phillips' posterior information criterion gives consistent results, while the results by Kleibergen and Paap method depends on the prior hyperparameters that we specify. In Chapter 3, we develop the cointegration model that allows cointegration relationships to be switched on and off depending on the regime. Unlike the classical method that requires a two-step estimation, the Bayesian method provide a straightforward estimation and testing procedure. In Chapter 4, we consider cointegration model with multiple structural breaks in the level, trend and error covariance. The more general model with breaks in both the adjustment term and the cointegrating vectors are also presented. To date, there is no research that deals with a cointegration model with unknown multiple structural breaks in any subset of the parameters.
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Feldkircher, Martin, and Florian Huber. "Adaptive Shrinkage in Bayesian Vector Autoregressive Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4933/1/wp221.pdf.
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Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this paper we derive the shrinkage prior of Griffin et al. (2010) for the VAR case and its relevant conditional posterior distributions. This
framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariances of the VAR along with Gamma priors on a set of local and global prior scaling parameters. This prior setup is then generalized by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. A simulation exercise shows that the proposed framework yields more precise estimates of the model parameters and impulse response functions. In addition, a forecasting exercise applied to US data shows that the proposed prior outperforms other specifications in terms of point and density predictions. (authors' abstract)Series: Department of Economics Working Paper Series
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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models /." Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1995. http://www.hhs.se/efi/summary/405.
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Alves, Vladimir da Costa. "Os efeitos dos ciclos da construção civil na atividade econômica do Brasil." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6871.
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Vladimir da Costa Alves_.pdf: 1430205 bytes, checksum: a7c55af3297cf449b13a07261f5a2586 (MD5)Made available in DSpace on 2018-02-07T12:55:00Z (GMT). No. of bitstreams: 1 Vladimir da Costa Alves_.pdf: 1430205 bytes, checksum: a7c55af3297cf449b13a07261f5a2586 (MD5) Previous issue date: 2017-11-30
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Este trabalho tem o objetivo de verificar a existência de ciclos na indústria da construção civil no Brasil e analisar seus efeitos em outras atividades da economia brasileira, bem como verificar a sua relevância no que se refere ao PIB deste país. O período analisado corresponde aos anos entre 1996 e 2016. A motivação para pesquisar este segmento reside na relevância da indústria da construção civil na economia brasileira, tanto pela sua magnitude na participação do PIB nacional quanto pelo seu grau de encadeamento na cadeia produtiva e, ainda, pelo posicionamento na atividade econômica como um todo. A metodologia utilizada para atingir os objetivos propostos foi o uso do filtro HP e do modelo VAR a partir dos dados publicados pelo IBGE. O filtro HP permitiu verificar o nível de correlação dos ciclos da construção civil com outros segmentos selecionados. Já o modelo VAR permitiu constatar os impactos específicos mais significativos de um choque de 1% no nível de atividade na própria construção civil e indústria extrativa mineral. De um modo geral, os resultados mostram que existe uma correlação mais forte com a indústria extrativa mineral, a indústria da transformação e o PIB total no Brasil.
This paper aims to verify the existence of cycles in the Brazilian construction industry and to analyze its effects on other activities of the Brazilian economy, as well as to verify its relevance regarding the GDP of this country. The period analyzed corresponds to the years between 1996 and 2016. The motivation to research this segment lies in the relevance of the civil construction industry in the Brazilian economy, both for its magnitude in the participation of the national GDP and for its degree of linkage in the productive chain and, still, by positioning in the economic activity as a whole. The methodology used to reach the proposed objectives was the use of the HP filter and the VAR model from the data published by IBGE. The HP filter allowed to verify the level of correlation of the construction cycles with other selected segments. On the other hand, the VAR model showed the most significant specific impacts of a 1% shock on the level of activity in the civil construction and mining industry. Overall, the results show that there is a stronger correlation with mineral extractive industry, manufacturing industry and total GDP in Brazil.
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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1995. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-878.
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In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. Their popularity appears to be attributable to their flexibility relative to other models rather than to their virtues per se. In addition, analysts often use VAR models as benchmark models. VAR modeling has not gone uncriticised, though. A list of relevant arguments against VAR modelling can be found in Section 2.3 of this thesis. There is one additional problem which is rarely mentioned though, namely the often heavily biased estimates in VAR models. Although methods to reduce this bias have been available for quite some time, it has probably not been done before, at least not in any systematic way. The present thesis attempts to systematically examine the performance of bias-reduced VAR estimates, using two existing and one newly derived approximation to the bias. The thesis is orginanised as follows. After a short introductory chapter, a brief history of VAR modelling can be found in Chapter 2 together with a review of different representations and a compilation of criticisms against VAR models. Chapter 3 reports the results of very extensive Monte Carlo experiments serving dual purposes: Firstly, the simulations will reveal whether or not bias really poses a serious problem, because if it turns out that biases appear only by exception or are mainly insignificant, there would be little need to reduce the bias. Secondly, the same data as in Chapter 3 will be used in Chapter 4 to evaluate the bias approximations, allowing for direct comparison between bias-reduced and original estimates. Though Monte Carlo methods have been (rightfully) criticised for being too specific to allow for any generalisation, there seems to be no good alternative to analyse small-sample properties of complicated estimators such as these. Chapter 4 is in a sense the core of the thesis, containing evaluations of three bias approximations. The performance of the bias approximations is evaluated chiefly using single regression equations and 3D surfaces. The only truly new research result in this thesis can also be found in Chapter 4; a second-order approximation to the bias of the parameter matrix in a VAR(p) model. Its performance is compared with the performance of two existing first-order approximations, and all three are used to construct bias-reduced estimators, which are then evaluated. Chapter 5 holds an application of US money supply and inflation in order to find out whether the results in Chapter 4 can have any real impacts. Unfortunately though, bias reduction appears not to make any difference in this particular case. Chapter 6 concludes.Diss. Stockholm : Handelshögsk.
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SANTOS, ALEXANDRE JOSE DOS. "TREE-STRUCTURE SMOOTH TRANSITION VECTOR AUTOREGRESSIVE MODELS – STVAR-TREE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=15888@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOREsta dissertação tem como objetivo principal introduzir uma formulação de modelo não-linear multivariado, a qual combina o modelo STVAR (Smooth Transition Vector Autoregressive) com a metodologia CART (Classification and Regression Tree) a fim de utilizá-lo para geração de cenários e de previsões. O modelo resultante é um Modelo Vetorial Auto-Regressivo com Transição Suave Estruturado por Árvores, denominado STVAR-Tree e tem como base o conceito de múltiplos regimes, definidos por árvore binária. A especificação do modelo é feita através do teste LM. Desta forma, o crescimento da árvore é condicionado à existência de não-linearidade nas séries, que aponta a divisão do nó e a variável de transição correspondente. Em cada divisão, são estimados os parâmetros lineares, por Mínimos Quadrados Multivariados, e os parâmetros não-lineares, por Mínimos Quadrados Não-Lineares. Como forma de avaliação do modelo STVARTree, foram realizados diversos experimentos de Monte Carlo com o objetivo de constatar a funcionalidade tanto do teste LM quanto da estimação do modelo. Bons resultados foram obtidos para amostras médias e grandes. Além dos experimentos, o modelo STVAR-Tree foi aplicado às séries brasileiras de Vazão de Rios e Preço Spot de energia elétrica. No primeiro estudo, o modelo foi comparado estatisticamente com o Periodic Autoregressive (PAR) e apresentou um desempenho muito superior ao concorrente. No segundo caso, a comparação foi com a modelagem Neuro-Fuzzy e ganhou em uma das quatro séries. Somando os resultados dos experimentos e das duas aplicações conclui-se que o modelo STVAR-Tree pode ser utilizado na solução de problemas reais, apresentando bom desempenho.
The main goal of the dissertation is to introduce a nonlinear multivariate model, which combines the model STVAR (Smooth Transition Vector Autoregressive) with the CART (Classification and Regression Tree) method and use it for generating scenarios and forecasting. The resulting model is a Tree- Structured Vector Autoregressive model with Smooth Transition, called STVARTree, which is based on the concept of multiple regimes, defined by binary tree. The model specification is based on Lagrange Multiplier tests. Thus, the growth of the tree is conditioned on the existence of nonlinearity in the time series, which indicates the node to be split and the corresponding transition variable. In each division, linear parameters are estimated by Multivariate Least Squares, and nonlinear parameters by Non-Linear Least Squares. As a way of checking the STVAR-Tree model, several Monte Carlo experiments were performed in order to see the functionality of both the LM test and the model estimation. Best results were obtained with medium and large samples. Besides, the STVAR-Tree model was applied to Brazilian time series of Rivers Flow and electricity spot price. In the first study, the model was statistically compared to the Periodic Autoregressive (PAR) model and had a much higher performance than the competitor. In the second case, the model comparison was with Neural-Fuzzy Modeling and the STVAR-Tree model won in one of the four series. Adding both the experiments and the two applications results we conclude that the STVARTree model may be applied to solve real problems, having good results.
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Crespo, Cuaresma Jesus, Martin Feldkircher, and Florian Huber. "Forecasting with Global Vector Autoregressive Models: A Bayesian Approach." Wiley, 2016. http://dx.doi.org/10.1002/jae.2504.
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This paper develops a Bayesian variant of global vector autoregressive (B-GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B-GVAR models in terms of point and density forecasts for one-quarter-ahead and four-quarter-ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country-specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B-GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country-specific vector autoregressions
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Fernandes, Pedro Manuel Ribeiro. "The role of banks in economic growth : an empirical application to Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19408.
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Mestrado em Economia Monetária e FinanceiraEsta dissertação avalia o contributo dos bancos para o crescimento económico em Portugal desde a adopção do Euro, usando testes de cointegração e causalidade, bem como funções de resposta a impulsos. Usando rácios de passivos líquidos (depósitos) dos bancos e empréstimos em percentagem do PIB nominal como medidas do desenvolvimento financeiro, encontramos forte evidência de que o crescimento económico exerce um impacto positivo no desenvolvimento financeiro, de acordo com Demetriades e Hussein (1996). Concluiu-se também que os empréstimos bancários não aumentam o produto real no longo e no curto prazo, também de acordo com Demetriades e Hussein (1996). Ao invés disso, estes têm um efeito negativo no PIB real per capita. Esses resultados corroboram a visão defendida por Robinson (1952), como citado em King e Levine (1993a) e Lucas (1988), de que o financiamento apenas evolui em resposta aos desenvolvimentos da economia.
This dissertation evaluates the role of banks in economic growth in Portugal since the adoption of the Euro, using cointegration and causality tests, as well as impulse response functions. Using ratios of banks? liquid liabilities (deposits) and loans to nominal GDP as a measure of financial development, we find strong evidence of economic growth exerting a positive impact on financial development, in line with Demetriades and Hussein (1996). It was also concluded that bank lending does not boost real output both in the long-run and in the short-run, also in line with Demetriades and Hussein (1996). Instead, it has a negative effect on real per capita GDP. These results support the view championed by Robinson (1952), as cited in King and Levine (1993a), and Lucas (1988), that finance only evolves in response to developments in the economy.
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Braun, Robin [Verfasser]. "Three Essays on Identification in Structural Vector Autoregressive Models / Robin Braun." Konstanz : KOPS Universität Konstanz, 2019. http://d-nb.info/1191693473/34.
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Van, Heerden Petrus Marthinus Stephanus. "The relationship between the forward– and the realized spot exchange rate in South Africa / Petrus Marthinus Stephanus van Heerden." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4511.
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The inability to effectively hedge against unfavourable exchange rate movements, using the
current forward exchange rate as the only guideline, is a key inhibiting factor of international
trade. Market participants use the current forward exchange rate quoted in the market to make
decisions regarding future exchange rate changes. However, the current forward exchange rate
is not solely determined by the interaction of demand and supply, but is also a mechanistic
estimation, which is based on the current spot exchange rate and the carry cost of the
transaction. Results of various studies, including this study, demonstrated that the current
forward exchange rate differs substantially from the realized future spot exchange rate. This
phenomenon is known as the exchange rate puzzle.
This study contributes to the dynamics of modelling exchange rate theories by developing an
exchange rate model that has the ability to explain the realized future spot exchange rate and
the exchange rate puzzle. The exchange rate model is based only on current (time t) economic
fundamentals and includes an alternative approach of incorporating the impact of the interaction
of two international financial markets into the model. This study derived a unique exchange rate
model, which proves that the exchange rate puzzle is a pseudo problem. The pseudo problem
is based on the generally excepted fallacy that current non–stationary, level time series data
cannot be used to model exchange rate theories, because of the incorrect assumption that all
the available econometric methods yield statistically insignificant results due to spurious
regressions. Empirical evidence conclusively shows that using non–stationary, level time series
data of current economic fundamentals can statistically significantly explain the realized future
spot exchange rate and, therefore, that the exchange rate puzzle can be solved.
This model will give market participants in the foreign exchange market a better indication of
expected future exchange rates, which will considerably reduce the dependence on the
mechanistically derived forward points. The newly derived exchange rate model will also have an influence on the demand and supply of forward exchange, resulting in forward points that are
a more accurate prediction of the realized future exchange rate.Thesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2011.
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Lim, Néhémy. "Estimation de modèles autorégressifs vectoriels à noyaux à valeur opérateur : Application à l'inférence de réseaux." Thesis, Evry-Val d'Essonne, 2015. http://www.theses.fr/2015EVRY0007/document.
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Dans l’analyse des séries temporelles multivariées, la plupart des modèles existants sont utilisés à des fins de prévision, c’est-à-dire pour estimer les valeurs futures du système étudié à partir d’un historique de valeurs observées dans le passé. Une autre tâche consiste à extraire des causalités entre les variables d’un système dynamique. C’est pour ce dernier problème à visée explicative que nous développons une série d’outils. À cette fin, nous définissons dans cette thèse une nouvelle famille de modèles autorégressifs vectoriels non paramétriques construits à partir de noyaux à valeur opérateur. En faisant l’hypothèse d’une structure sous-jacente creuse, la parcimonie du modèle est contrôlée en imposant dans la fonction de coût des contraintes de parcimonie aux paramètres du modèle (qui sont en l’occurrence des vecteurs qui pondèrent une combinaison linéaire de noyaux). Les noyaux étudiés possèdent parfois des hyperparamètres qui doivent être appris selon la nature du problème considéré. Lorsque des hypothèses de travail ou des connaissances expertes permettent de fixer les paramètres du noyau, le problème d’apprentissage se réduit à la seule estimation des paramètres du modèle. Pour optimiser la fonction de coût correspondante, nous développons un algorithme proximal. A contrario, lorsqu’aucune hypothèse relative aux variables n’est disponible, les paramètres de certains noyaux ne peuvent être fixés a priori. Il est alors nécessaire d’apprendre conjointement les paramètres du modèle et ceux du noyau. Pour cela, nous faisons appel à un schéma d’optimisation alterné qui met en jeu des méthodes proximales. Nous proposons ensuite d’extraire un estimateur de la matrice d’adjacence encodant le réseau causal sous-jacent en calculant une statistique des matrices jacobiennes instantanées. Dans le cas de la grande dimension, c’est-à-dire un nombre insuffisant de données par rapport au nombre de variables, nous mettons en oeuvre une approche d’ensemble qui partage des caractéristiques du boosting et des forêts aléatoires. Afin de démontrer l’efficacité de nos modèles, nous les appliquons à deux jeux de données : des données simulées à partir de réseaux de régulation génique et des données réelles sur le climatIn multivariate time series analysis, existing models are often used for forecasting, i.e. estimating future values of the observed system based on previously observed values. Another purpose is to find causal relationships among a set of state variables within a dynamical system. We focus on the latter and develop tools in order to address this problem. In this thesis, we define a new family of nonparametric vector autoregressive models based on operator-valued kernels. Assuming a sparse underlying structure, we control the model’s sparsity by defining a loss function that includes sparsity-inducing penalties on the model parameters (which are basis vectors within a linear combination of kernels). The selected kernels sometimes involve hyperparameters that may need to be learned depending on the nature of the problem. On the one hand, when expert knowledge or working assumptions allow presetting the parameters of the kernel, the learning problem boils down to estimating only the model parameters. To optimize the corresponding loss function, we develop a proximal algorithm. On the other hand, when no prior knowledge is available, some other kernels may exhibit unknown parameters. Consequently, this leads to the joint learning of the kernel parameters in addition to the model parameters. We thus resort to an alternate optimization scheme which involves proximal methods. Subsequently, we propose to build an estimate of the adjacency matrix coding for the underlying causal network by computing a function of the instantaneous Jacobian matrices. In a high-dimensional setting, i.e. insufficient amount of data compared to the number of variables, we design an ensemble methodology that shares features of boosting and random forests. In order to emphasize the performance of the developed models, we apply them on two tracks : simulated data from gene regulatory networks and real climate data
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Bertsche, Dominik [Verfasser]. "Three Essays on Identification and Dimension Reduction in Vector Autoregressive Models / Dominik Bertsche." Konstanz : KOPS Universität Konstanz, 2020. http://d-nb.info/1209879778/34.
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Kobler, Alexander. "Sources and dynamics of macroeconomic fluctuations in Switzerland : evidence from a structural vector autoregressive approach /." Bern ; Berlin ; Bruxelles [etc.] : P. Lang, 2000. http://aleph.unisg.ch/hsgscan/hm00001729.pdf.
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Karl, Velander, and Callerud Karin. "The development of the financialsystem and economic growth in Sweden : A Granger causality analysis." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78703.
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Sharp, Gary David. "Lag length selection for vector error correction models." Thesis, Rhodes University, 2010. http://hdl.handle.net/10962/d1002808.
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This thesis investigates the problem of model identification in a Vector Autoregressive framework. The study reviews the existing research, conducts an extensive simulation based analysis of thirteen information theoretic criterion (IC), one of which is a novel derivation. The simulation exercise considers the evaluation of seven alternative error restricted vector autoregressive models with four different lag lengths. Alternative sample sizes and parameterisations are also evaluated and compared to results in the existing literature. The results of the comparative analysis provide strong support for the efficiency based criterion of Akaike and in particular the selection capability of the novel criterion, referred to as a modified corrected Akaike information criterion, demonstrates useful finite sample properties.
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naz, saima. "Forecasting daily maximum temperature of Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112404.
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The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines. The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI).
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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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Wong, Kin-man, and 黃健文. "A vector autoregression (VAR) model of housing starts and housing price in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hdl.handle.net/10722/194603.
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It is observed that there are many different models about housing price. Yet, this is relatively smaller number of studies about housing starts. This thesis is an empirical study to work out the relationship between housing starts, housing price and other economic and policy instrumental factors. To achieve this objective, a Vector Autoregression (VAR) model is built since there is inter-relationship between housing starts and housing price.
By applying previous models filled with the research gaps, a new VAR model about the housing starts and housing price in Hong Kong is built. Four hypotheses are tested in the thesis. The first and second hypotheses are if housing starts and housing price are affected by the given exogenous variables. The third hypothesis is if the past movement of economic variables reliable in predicting future values of that variable. The last hypothesis is to test if the “high-land-price” policy really pushes up the housing price.
The empirical results found in this thesis are a little bit different to previous studies in Hong Kong and overseas. Factors which are frequently proved to be statistically significant are not significant in this study (e.g. interest rate and tender price index). Developers in Hong Kong are found to care more about the future market rather than the current market conditions. Many factors do not exert an influence directly on housing starts but indirectly through their impact to the change of the change of the housing price.
It is interesting to know that housing starts react negatively to a change in housing price. An increase in the change of housing price is a bullish signal for the developers. They will hold the land for a while until they expect the peak is coming upon the completion of a project. Therefore, the empirical results suggest the government has to introduce some policies which will lead to a fall in housing price in case that she wants to increase the supply of new private residential housing. Developers will accelerate the applications to commence construction when they expect there will be a downward trend in the housing price (which is shown by a negative change of the housing price..published_or_final_version
Real Estate and Construction
Master
Master of Philosophy
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Silber, Frank. "Makroökonometrische Anpassungsanalyse im Vector-Error-Correction-Model (VECM) : Untersuchungen an ausgewählten Arbeitsmärkten /." Frankfurt am Main: Lang, 2003. http://www.gbv.de/dms/zbw/362076561.pdf.
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Podstawski, Maximilian [Verfasser]. "Unconventional Identification in Vector Autoregressive Models: Empirical Essays on Credit, Risk and Uncertainty / Maximilian Podstawski." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1113593113/34.
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Ma, Nicholas. "Stationary time series resulting from certain positive definite kernels and simulation via high-order vector autoregressive models." Thesis, Wichita State University, 2013. http://hdl.handle.net/10057/6826.
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Vector auto-regressive models have traditionally been used to model and forecast
multivariate time series data, predicting future values based on previous observations. In this
thesis, we introduce some multivariate time series with power-law decaying covariance matrix
functions, and then construct a VAR model in order to generate approximate data from that
time series. A fast model is developed to solve for the VAR(p) coefficients, implementing
a block-Toeplitz equation solver to enable the choice of large p, avoiding the memory and
speed issues with solving large systems via Gaussian elimination. An approximate error
bound is established, demonstrating the quality of the simulation for large p. Finally, we
explore inverting the VAR model to obtain a VMA (vector moving average) model.Thesis (M.S.)--Wichita State University, Fairmount College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics and Physics