Master of Science / Department 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.

We propose a large-scale Bayesian VAR model with factor stochastic volatility to investigate the macroeconomic consequences of international uncertainty shocks on the G7 countries. The factor structure enables us to identify an international uncertainty shock by assuming that it is the factor most correlated with forecast errors related to equity markets and permits fast sampling of the model. Our findings suggest that the estimated uncertainty factor is strongly related to global equity price volatility, closely tracking other prominent measures commonly adopted to assess global uncertainty. The dynamic responses of a set of macroeconomic and financial variables show that an international uncertainty shock exerts a powerful effect on all economies and variables under consideration. / Series: Department of Economics Working Paper Series

We compare two time series models: an ARMA(1,1)-ACD(1,1)-NIG model against an ARMA(1,1)-GARCH(1,1)-NIG model. Their out-of-sample performance is of interest rather than their in-sample properties. The models produce one-day ahead forecasts which are evaluated using three statistical tests: VaR-test, VaRdur-test and Berkowitz-test. All three tests are concerned with the the tail events, since our time series models are often used to estimate downside risk. When the two models are applied to data on Canadian stock market returns, our three statistical tests point in the direction that the ACD model and the GARCH model perform similarly. The difference between the models is small. We finish with comments on the model uncertainty inherit in the comparison.

M.Com. (Economic Development and Policy Issues) / This research analyses the effect of exchange rate volatility on exports using a sample of nine emerging countries – Argentina, Brazil, India, Indonesia, Mexico, Malaysia, Poland, South Africa and Thailand – between 1995 and 2010. The study uses panel data models, with a standard exports equation with exports performance determined by exchange rate volatility, the level of exchange rate, demand conditions in major countries as well as terms of trade. Exchange rate volatility is measured by Generalised Autoregressive Conditional Heteroscedasticity (GARCH) and conventional standard deviation in order to determine if the instrument of volatility used influences the nature of the relationship between exchange rate volatility and exports. The results show that exchange rate volatility has a significant negative effect on the performance of exports regardless of the measure of volatility used. The Pedroni residual cointegration method was used to test for panel cointegration to determine if there is a long-run relationship among the variables, and the test showed that a long-run relationship does exists. Generally, the study concludes that policy mix that will reduce exchange rate volatility (such as managed exchange rate regimes) and relatively competitive exchange rates are essential for emerging markets in order to sustain their exports performance.

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

O diagnóstico de doenças neuromusculares é feito pelo uso conjunto de várias ferramentas. Dentre elas, o exame de eletromiografia clínica fornece informações vitais ao diagnóstico. A aplicação de alguns classificadores (discriminante linear e redes neurais artificiais) aos diversos parâmetros dos sinais de eletromiografia (número de fases, de reversões e de cruzamentos de zero, freqüência mediana, coeficientes auto-regressivos) tem fornecido resultados promissores na literatura. No entanto, a necessidade de um número grande de coeficientes auto-regressivos direcionou este mestrado ao uso de modelos auto-regressivos de média móvel com um número menor de coeficientes. A classificação (em normal, neuropático ou miopático) foi feita pela máquina de vetor de suporte, um tipo de rede neural artificial de uso recente. O objetivo deste trabalho foi o de estudar a viabilidade do uso de modelos auto-regressivos de média móvel (ARMA) de ordem baixa, em vez de auto-regressivos de ordem alta, em conjunção com a máquina de vetor de suporte, para auxílio ao diagnóstico. Os resultados indicam que a máquina de vetor de suporte tem desempenho melhor que o discriminante linear de Fisher e que os modelos ARMA(1,11) e ARMA(1,12) fornecem altas taxas de classificação (81,5%), cujos valores são próximos ao máximo obtido com modelos auto-regressivos de ordem 39. Portanto, recomenda-se o uso da máquina de vetor de suporte e de modelos ARMA (1,11) ou ARMA(1,12) para a classificação de sinais de eletromiografia de agulha, de 800ms de duração e amostrados a 25kHz. / The diagnosis of neuromuscular diseases is attained by the combined use of several tools. Among these tools, clinical electromyography provides key information to the diagnosis. In the literature, the application of some classifiers (linear discriminant and artificial neural networks) to a variety of electromyography parameters (number of phases, turns and zero crossings; median frequency, auto-regressive coefficients) has provided promising results. Nevertheless, the need of a large number of auto-regressive coefficients has guided this Master\'s thesis to the use of a smaller number of auto-regressive moving-average coefficients. The classification task (into normal, neuropathic or myopathic) was achieved by support vector machines, a type of artificial neural network recently proposed. This work\'s objective was to study if low-order auto-regressive moving-average (ARMA) models can or cannot be used to substitute high-order auto-regressive models, in combination with support vector machines, for diagnostic purposes. Results point that support vector machines have better performance than Fisher linear discriminants. They also show that ARMA(1,11) and ARMA(1,12) models provide high classification rates (81.5%). These values are close to the maximum obtained by using 39 auto-regressive coefficients. So, we recommend the use of support vector machines and ARMA(1,11) or ARMA(1,12) to the classification of 800ms needle electromyography signals acquired at 25kHz.

Os modelos autoregressivos simétricos são modelos de regressão em que os erros são correlacionados -- AR(1) -- e pertencem à classe de distribuições simétricas. O objetivo deste trabalho é discutir métodos de diagnóstico de influência para esses modelos. Para ilustrar a metodologia, são apresentados exemplos do modelo de precificação de ativos (CAPM). / The symmetric autoregressive models are regression models in which the errors are correlated and belong to the class of symmetrical distributions. The aim of this work is to discuss influence diagnostic methods for those models. To illustrate the methodology, examples of Capital Asset Pricing Models (CAPM) are presented.

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

Housing prices are of crucial importance in financial stability management. The severe financial crises that originated in the housing market in the US and subsequently spread throughout the world highlighted the crucial role that the housing market plays in preserving financial stability. After the severe housing market crash, many financial institutions in the US suffered from high default rates, severe liquidity shortages, and even bankruptcy. Against this background, researchers have sought to use econometric models to capture and forecast prices of homes. Available empirical research indicates that nonlinear models may be suitable for modelling price cycles. Accordingly, this thesis focuses primarily on using nonlinear models to empirically investigate cyclical patterns in housing prices. More specifically, the content of this thesis can be summarised in three essays which complement the existing literature on price modelling by using nonlinear models. The first essay contributes to the literature by testing the ability of regime switching models to capture and forecast house prices. The second essay examines the impact of banking factors on house price fluctuations. To account for house price characteristics, the regime switching model and generalised autoregressive conditionally heteroscedastic (GARCH) in-mean model have been used. The final essay investigates the effect of structural breaks on the unit root test and shows that a time-varying GARCH in-mean model can be used to estimate the housing price cycle in the UK.

In this paper, we estimate a Bayesian vector autoregressive (VAR) model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area (EA). This allows us to incorporate uncertainty directly into the econometric framework and treat it as a latent quantity. Only a limited number of papers estimates impacts of uncertainty and macroeconomic consequences jointly, and most literature in this sphere is based on single countries. We analyze the special case of a shock restricted to the Euro area, whose countries are highly related by definition. Among other variables, we find significant results of a decrease in real activity measured by GDP in most Euro area countries over a period of roughly a year following an uncertainty shock. / Series: Department of Economics Working Paper Series