Thesis (doctoral)--Universiteit van Amsterdam, 1998. / Includes bibliographical references (p. 233-249).

This dissertation consists of two essays. The first essay focuses on developing a quantitative theory for a small open economy dynamic stochastic general equilibrium (DSGE) model with a housing sector allowing for both contemporaneous and news shocks. The second essay is an empirical study on the macroeconomic forecasting using both structural and non-structural models. In the first essay, we develop a DSGE model with a housing sector, which incorporates both contemporaneous and news shocks to domestic and external fundamentals, to explore the kind of and the extent to which different shocks to economic fundamentals matter for driving housing market dynamics in a small open economy. The model is estimated by the Bayesian method, using data from Hong Kong. The quantitative results show that external shocks and news shocks play a significant role in this market. Contemporaneous shock to foreign housing preference, contemporaneous shock to terms of trade, and news shocks to technology in the consumption goods sector explain one-third each of the variance of housing price. Terms of trade contemporaneous shock and consumption technology news shocks also contribute 36% and 59%, respectively, to the variance in housing investment. The simulation results enable policy makers to identify the key driving forces behind the housing market dynamics and the interaction between housing market and the macroeconomy in Hong Kong. In the second essay, we compare the forecasting performance between structural and non-structural models for a small open economy. The structural model refers to the small open economy DSGE model with the housing sector in the first essay. In addition, we examine various non-structural models including both Bayesian and classical time-series methods in our forecasting exercises. We also include the information from a large-scale quarterly data series in some models using two approaches to capture the influence of fundamentals: extracting common factors by principal component analysis in a dynamic factor model (DFM), factor-augmented vector autoregression (FAVAR), and Bayesian FAVAR (BFAVAR) or Bayesian shrinkage in a large-scale vector autoregression (BVAR). In this study, we forecast five key macroeconomic variables, namely, output, consumption, employment, housing price inflation, and CPI-based inflation using quarterly data. The results, based on mean absolute error (MAE) and root mean squared error (RMSE) of one to eight quarters ahead out-of-sample forecasts, indicate that the non-structural models outperform the structural model for all variables of interest across all horizons. Among the non-structural models, small-scale BVAR performs better with short forecasting horizons, although DFM shows a similar predictive ability. As the forecasting horizon grows, DFM tends to improve over other models and is better suited in forecasting key macroeconomic variables at longer horizons.

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished

M.Ing. (Engineering Management) / As a result of its direct relations with the different sectors of the economy, the construction industry is used as a tool by governments around the world for economic recovery. The South African government has taken such a stance following the 2008/2009 global financial crisis, by proposing the government‘s infrastructure spending and expansion programme for economic growth and development. The South African economy took a stalling from 4.1% in 2007 to 2.3% in 2008 and then turning negative in 2009. This research paper seeks to gather insight of the relationship between construction output and economic growth, as well as the potential long and short term benefits of major infrastructure projects to the South African economy. South Africa; as a developing country and with its construction industry currently in the upward trend, there are lessons that can be learnt from the developed countries. To this end, trends in construction output and GDP have been scrutinised to examine any pattern of development relating the construction industry and its relationship with the economy as a whole. The examination spanned over 32 countries grouped according to their respective development status. With South Africa‘s significant increase in the annual change in construction output between 2004 and 2008, amid developments of the FIFA world cup, confirmed the relation between construction investment and economic growth. The contribution of the construction industry to GDP for developed countries all follow the same trend of having high contributions in the beginning of development declining as less and less new infrastructure is required by the country. The contribution of construction averaged at 6 per cent for the entire duration of analysis between 1970 and 2011. Compared to international standards, South Africa‘s contribution of construction to GDP is determined to be very low at 3 per cent for the duration 1963 and 2011. Further, the construction industry has displayed characteristics of instability for South Africa when compared to developed countries over the same period.

Asset prices are forward looking. Therefore, expectations play a central role in shaping asset prices. In this dissertation, I challenge the rational expectation assumption that has been influential in the field of asset pricing over the past few decades. Different from previous approaches, which typically build on behavioral theories originated from psychology literature, my approach takes data on subjective beliefs seriously and proposes empirically grounded models of subjective beliefs to evaluate the merits of the rational expectation assumption. Specifically, this dissertation research: 1). collects and analyzes data on investors' actual subjective return expectations; 2). builds models of subjective expectation formation; 3). derives and tests the models' implications for asset prices. I document the results of the research in two chapters. In summary, the dissertation shows that investors do not hold full-information rational expectations. On the other hand, their subjective expectations are not necessarily irrational. Rather, they are bounded by the information environment investors face and reflect investors' personal experiences and preferences. The deviation from fully-rational expectations can explain asset pricing anomalies such as cross-sectional anomalies in the U.S. stock market. In the first chapter, I provide a framework to rationalize the evidence of extrapolative return expectations, which is often interpreted as investors being irrational. I first document that subjective return expectations of Wall Street (sell-side, buy-side) analysts are contrarian and counter-cyclical. I then highlight the identification problem investors face when theyform return expectations using imperfect predictors through Kalman Filters. Investors differ in how they impose subjective priors, the same way rational agents differ in different macro-finance models. Estimating the priors using surveys, I find Wall Street and Main Street (CFOs, pension funds) both believe persistent cash flows drive asset prices but disagree on how fundamental news relates to future returns. These results support models featuring heterogeneous agents with persistent subjective growth expectations. In the second chapter, I propose and test a unifying hypothesis to explain both cross-sectional return anomalies and subjective return expectation errors: some investors falsely ignore the dynamics of discount rates when forming return expectations. Consistent with the hypothesis: 1) stocks' expected cash flow growth and idiosyncratic volatility explain significant cross-sectional variation of analysts' return forecast errors; 2). a measure of mispricing at the firm level strongly predicts stock returns, even among stocks in the S&P500 and at long horizon; 3). a tradable mispricing factor explains the CAPM alphas of 12 leading anomalies including investment, profitability, beta, idiosyncratic volatility and cash flow duration.

Leveraging the knowledge of an organization is an ongoing challenge that has given rise to the field of knowledge management. Yet, despite spending enormous sums of organizational resources on Information Technology (IT) systems, executives recognize there is much more knowledge to harvest. Prediction markets are emerging as one tool to help extract this tacit knowledge and make it operational. Yet, prediction markets, like other markets, are subject to pathologies (e.g., bubbles and crashes) which compromise their accuracy and may discourage organizational use. The techniques of experimental economics were used to study the characteristics of prediction markets. Empirical data was gathered from an on-line asynchronous prediction market. Participants allocated tickets based on private information and, depending on the market type, public information indicative of how prior participants had allocated their tickets. The experimental design featured three levels of feedback (no-feedback, percentages of total allocated tickets and frequency of total allocated tickets) presented to the participants. The research supported the hypothesis that information assimilation in feedback markets is composed of two mechanisms - information collection and aggregation. These are defined as: Collection - The compilation of dispersed information - individuals using their own private information make judgments and act accordingly in the market. Aggregation - The market's judgment on the implications of this gathered information - an inductive process. This effect comes from participants integrating public information with their private information in their decision process. Information collection was studied in isolation in no feedback markets and the hypothesis that markets outperform the average of their participants was supported. The hypothesis that with the addition of feedback, the process of aggregation would be present was also supported. Aggregation was shown to create agreement in markets (as measured by entropy) and drive market results closer to correct values (the known probabilities). However, the research also supported the hypothesis that aggregation can lead to information mirages, creating a market bubble. The research showed that the presence and type of feedback can be used to modulate market performance. Adding feedback, or more informative feedback, increased the market's precision at the expense of accuracy. The research supported the hypotheses that these changes were due to the inductive aggregation process which creates agreement (increasing precision), but also occasionally generates information mirages (which reduces accuracy). The way individual participants use information to make allocations was characterized. In feedback markets the fit of participants' responses to various decision models demonstrated great variety. The decision models ranged from little use of information (e.g., MaxiMin), use of only private information (e.g., allocation in proportion to probabilities), use of only public information (e.g., allocating in proportion to public distributions) and integration of public and private information. Analysis of all feedback market responses using multivariate regression also supported the hypothesis that public and private information were being integrated by some participants. The subtle information integration results are in contrast to the distinct differences seen in markets with varying levels of feedback. This illustrates that the differences in market performance with feedback are an emergent phenomenon (i.e., one that could not be predicted by analyzing the behavior of individuals in different market situations). The results of this study have increased our collective knowledge of market operation and have revealed methods that organizations can use in the construction and analysis of prediction markets. In some situations markets without feedback may be a preferred option. The research supports the hypothesis that information aggregation in feedback markets can be simultaneously responsible for beneficial information processing as well as harmful information mirage induced bubbles. In fact, a market subject to mirage prone data resembles a Prisoner's Dilemma where individual rationality results in collective irrationality.

The need for accurate forecasting and its potential benefits are well established in the literature. Virtually all individuals and organizations have at one time or another made decisions based on forecasts of future events. This widespread need for accurate predictions has resulted in considerable growth in the science of forecasting. To a large degree, practitioners are heavily dependent on academicians for generating new and improved forecasting techniques. In response to an increasingly dynamic environment, diverse and complex forecasting methods have been proposed to more accurately predict future events. These methods, which focus on the different characteristics of historical data, have ranged in complexity from simplistic to very sophisticated mathematical computations requiring a high level of expertise. By combining individual techniques to form composite forecasts in order to improve on the forecasting accuracy, researchers have taken advantage of the various strengths of these techniques. A number of combining methods have proven to yield better forecasts than individual methods, with the complexity of the various combining methods ranging from a simple average to quite complex weighting schemes. The focus of this study is to examine the usefulness of neural networks in composite forecasting. Emphasis is placed on the effectiveness of two neural networks (i.e., a backpropagation neural network and a modular neural network) relative to three traditional composite models (i.e., a simple average, a constrained mathematical programming model, and an unconstrained mathematical programming model) in the presence of four penalty cost functions for forecasting errors. Specifically, the overall objective of this study is to compare the shortterm predictive ability of each of the five composite forecasting techniques on various first-order autoregressive models, taking into account penalty cost functions representing four different situations. The results of this research suggest that in the vast majority of scenarios examined in this study, the neural network model clearly outperformed the other composite models. / Ph. D.

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This paper examines the impact of the recent global financial crisis on economic growth in developing economies and South Africa in particular. It explores whether the events experienced by developing countries conform to what would be anticipated from economic theory. This is done by firstly comparing country growth forecasts for 2012 captured in 2008 at the beginning of the crisis to actual 2012 GDP growth data. Secondly, panel data analysis is used to investigate three important transmission channels, namely those of Trade, Capital Flows and Exchange Rates for 25 developing economies. The results suggest that economic forecasters in 2008 on average overestimated GDP growth for 2012 by -21.6 percent (excluding Venezuela). The only important transmission channel identified using Trend analysis to explain this negative impact on growth was capital flows. However when using Panel regression analysis all three channels were found to explain the economic impact of the crisis on GDP growth for developing countries, conforming to economic theory. It was discovered that, contrary to what was initially expected, portfolio inflows actually increased for most developing countries during the crisis. This possibly can be explained by the impact of quantitative easing in the USA. South Africa was found to have been negatively impacted by the global financial crisis, but to a lesser extent when compared to most other developing countries. The findings are important for global investors looking for new investment opportunities. The extent to which individual economies are “decoupled” from developed economies’ performance provides possible opportunities for diversifying risk through a geographic spread of investor portfolios.