The objective of this thesis is to develop alternative forms of Dynamic Stochastic General Equilibrium (DSGE) models for forecasting the South African economy and, in turn, compare them with the forecasts generated by the Classical and Bayesian variants of the Vector Autoregression Models (VARs). Such a comparative analysis is aimed at developing a small-scale micro-founded framework that will help in forecasting the key macroeconomic variables of the economy. The thesis consists of three independent papers. The first paper develops a small-scale DSGE model based on Hansen's (1985) indivisible labor Real Business Cycle (RBC) model. The results suggest that, compared to the VARs and the Bayesian VARs, the DSGE model produces large out-of-sample forecast errors. In the basic RBC framework, business cycle fluctuations are purely driven by real technology shocks. This one-shock assumption makes the RBC models stochastically singular. In order to overcome the singularity problem in the RBC model developed in the first paper, the second paper develops a hybrid model (DSGE-VAR), in which the theoretical model is augmented with unobservable errors having a VAR representation. The model is estimated via maximum likelihood technique. The results suggest DSGE-VAR model outperforms the Classical VAR, but not the Bayesian VARs. However, it does indicate that the forecast accuracy can be improved alarmingly by using the estimated version of the DSGE model. The third paper develops a micro-founded New-Keynesian DSGE (NKDSGE) model. The model consists of three equations, an expectational IS curve, a forward-looking version of the Phillips curve, and a Taylor-type monetary policy rule. The results indicate that, besides the usual usage for policy analysis, a small-scale NKDSGE model has a future for forecasting. The NKDSGE model outperforms both the Classical and Bayesian variants of the VARs in forecasting inflation, but not for output growth and the nominal short-term interest rate. However, the differences of the forecast errors are minor. The indicated success of the NKDSGE model for predicting inflation is important, especially in the context of South Africa - an economy targeting inflation. / Thesis (PhD (Economics))--University of Pretoria, 2008. / Economics / unrestricted

The research aims to investigate the relationship between economic freedom, political freedom and economic growth. The Arab Spring placed renewed interest on the topic of freedom, yet current economic conditions seemingly contradicted the established theory. The largest free economies were being outperformed by those with less political and economic freedom.Three objectives were specified to answer the research question. The first objective aimed to determine the association between economic freedom, political freedom and economic growth, for which Spearman’s correlation was used. The second objective aimed to investigate causal relationships between the variables, for which Granger’s causality was employed. The third objective aimed to examine complex relationships between the variables, for which vector autoregression was used.Economic growth was weakly correlated with the independent variables. Civil liberties, political rights and economic freedom, however, had strong correlations with each other. Economic freedom and economic growth had bi-directional Granger-causality. Political rights Granger-caused economic freedom whilst civil liberties Granger-caused political rights and economic freedom. Using vector autoregression, the model consisting of economic growth, economic freedom and civil liberties had the greatest explanatory power towards economic growth. Existing theory therefore remains valid: political freedom enhances economic freedom, which, in turn, enhances economic growth.The relationship between economic freedom, political freedom and economic growth / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

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The diploma thesis investigates the effect of oil price fluctuations on selected macroeconomic indicators for a set of four oil-producing countries. It provides a general overview of the development in oil prices as well as the oil production and presents a more thorough analysis of the oil production, consumption and trade in the selected countries. It utilizes the methods of Vector Autoregression, Granger causality and Impulse response via the econometric software Gretl in the analysis of the effects and compares the current literature with quantitative results.

The big-box retail stores such as Wal-Mart and Target have become the focus of many studies researching their impacts on local economic outcomes. This dissertation studies three related topics: (i) the dynamic interrelationship among the presence of the big-box stores, retail wage, and employment, (ii) the impact of the big-box retailers on personal income growth, and (iii) the dynamic interrelationship between the presence of big-box retailers and personal income growth. The research draws important insights with potential implications for regional developers and policy makers. The first essay analyzes the dynamic relationship among the presence of the big-box retailers, retail wage, and employment at the county level for 1986-2005. A vector autoregression model is applied on panel data. Impulse response functions and variance decompositions are also presented. Results suggest that the presence of big-box stores decreases retail wages and increases retail employment. Retail employment has a higher impact on the retailers’ location decision than retail wage. The results also show that the presence of Wal-Mart drives the above-mentioned effects, while the presence of Target is insignificant. The second essay investigates the impact from the presence of big-box retailers on personal income growth in U.S. counties between 2000 and 2005 - based on neoclassical growth models of cross-country income convergence. Results suggest that counties having both Wal-Mart and Target stores experienced slower growth in personal income. After controlling for spatial autocorrelation, similar to the first essay, the effect of Wal-Mart’s presence on personal income growth is dominant in terms of statistical significance relative to Target’s. The third essay expands the second essay and investigates the dynamic interaction between the presence of big-box retailers and personal income growth over time at the county level for the period 1987-2005, using a panel vector autoregression model. For this analysis, the earning shares of natural resources and manufacturing sectors are included - assuming that all the variables are endogenous to one another. The findings indicate that big-box retailers negatively affect personal income growth, which is consistent with the second essay. However, personal income growth has an insignificant effect on the big-box retailers’ location decision.

The Swedish office markets has been emerging the last decade towards a higher rental level equilibrium. The aim of this study is to investigate the fundamental drivers of office rents and modelling of office rent forecasts in five Swedish office submarkets; Stockholm (2), Gothenburg (2) and Malmö (1). The methodology is a combination of economic theory and econometric analysis. The product is an econometric model. By using the estimated drivers, office rent forecasts are modelled and computed based on a vector autoregression-model. Our results show that office stock and vacancy, in lagged fashion, are statistically superior in explaining office rent development. OMX30 was evident to be the largest macro-driver in explaining office rent. The generated forecasts were significant and valid in the CBD-submarkets. However, the forecasts for the Rest of Inner City (RIC)-submarkets were not as precise. The results also show that the forecasts move more linearly compared to the actual office rent data that move more "step-wise".

This dissertation will focus on three research projects: Neighborhood vector auto regression in multivariate time series, uncertainty quantification for agent-based modeling networked anagrams, and a scalable algorithm for multi-class classification. The first project studies the modeling of multivariate time series, with the applications in the environmental sciences and other areas. In this work, a so-called neighborhood vector autoregression (NVAR) model is proposed to efficiently analyze large-dimensional multivariate time series. The time series are assumed to have underlying distances among them based on the inherent setting of the problem. When this distance matrix is available or can be obtained, the proposed NVAR method is demonstrated to provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real application of stream nitrogen study. The second project focuses on the study of group anagram games. In a group anagram game, players are provided letters to form as many words as possible. In this work, the enhanced agent behavior models for networked group anagram games are built, exercised, and evaluated under an uncertainty quantification framework. Specifically, the game data for players is clustered based on their skill levels (forming words, requesting letters, and replying to requests), the multinomial logistic regressions for transition probabilities are performed, and the uncertainty is quantified within each cluster. The result of this process is a model where players are assigned different numbers of neighbors and different skill levels in the game. Simulations of ego agents with neighbors are conducted to demonstrate the efficacy of the proposed methods. The third project aims to develop efficient and scalable algorithms for multi-class classification, which achieve a balance between prediction accuracy and computing efficiency, especially in high dimensional settings. The traditional multinomial logistic regression becomes slow in high dimensional settings where the number of classes (M) and the number of features (p) is large. Our algorithms are computing efficiently and scalable to data with even higher dimensions. The simulation and case study results demonstrate that our algorithms have huge advantage over traditional multinomial logistic regressions, and maintains comparable prediction performance. / Doctor of Philosophy / In many data-central applications, data often have complex structures involving temporal structures and high dimensionality. Modeling of complex data with temporal structures have attracted great attention in many applications such as enviromental sciences, network sciences, data mining, neuroscience, and economics. However, modeling such complex data is quite challenging due to large uncertainty and dimensionality of complex data. This dissertation focuses on modeling and prediction of complex data with temporal structures. Three different types of complex data are modeled. For example, the nitrogen of multiple streams are modeled in a joint manner, human actions in networked group anagrams are modeled and the uncertainty is quantified, and data with multiple labels are classified. Different models are proposed and they are demonstrated to be efficient through simulation and case study.

The grid of the future will be more decentralized due to the significant increase in distributed generation, and microgrids. In addition, due to the proliferation of large-scale intermittent wind power, the randomness in power system state will increase to unprecedented levels. This dissertation proposes a new state transition model for power system forecasting-aided state estimation, which aims at capturing the increasing stochastic nature in the states of the grid of the future. The proposed state forecasting model is based on time-series modeling of filtered system states and it takes spatial correlation among the states into account. Once the states with high spatial correlation are identified, the time-series models are developed to capture the dependency of voltages and angles in time and among each other. The temporal correlation in power system states (i.e. voltage angles and magnitudes) is modeled by using autoregression, while the spatial correlation among the system states (i.e. voltage angles) is modeled using vector autoregression. Simulation results show significant improvement in power system state forecasting accuracy especially in presence of distributed generation and microgrids. / Ph. D.

With economic uncertainties on the rise, understanding the relationship between consumer price inflation and consumer confidence becomes increasingly vital. This thesis investigates the relationship between Consumer Price Inflation (CPI) and Consumer confidence, specifically within the context of Sweden. The relationship is examined through the Vector Autoregression (VAR) model, spanning from the period 2002 to 2023. Drawing upon existing literature and theoretical frameworks in economics and psychology, this research provides a deeper understanding of the relationship between the two variables. By incorporating data up to 2023, this thesis will also examine the effects of the Covid-19 pandemic on the relationship between these key macroeconomic indicators. The results show statistically significant evidence that consumer price inflation predicts consumer confidence. Thus, we conclude that consumer price inflation plays a significant role in the dynamics of consumer confidence, influencing both economic conditions and expectations.

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