by Chan Ka Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 64-65). / Abstracts in English and Chinese. / ACKNOWLEDGMENTS --- p.iii / LIST OF TABLES --- p.iv / CHAPTER / Chapter I --- INTRODUCTION --- p.1 / Chapter II --- THE LITERATURE REVIEW --- p.4 / Chapter III --- METHODOLOGY / Forecast Pooling Technique / Modified Technique / Chapter IV --- MODEL SPECIFICATIONS --- p.16 / The Monthly Models / The Quarterly Model / Data Description / Chapter V --- THE COMBINED FORECAST --- p.32 / Pooling Forecast Technique in Case of Hong Kong / The Forecasts Results / Chapter VI --- CONCLUSION --- p.38 / TABLES --- p.40 / APPENDIX --- p.53 / BIBLIOGRAPHY --- p.64

Cheung Hiu-Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / ACKNOWLEGMENTS --- p.iii / LIST OF TABLES --- p.iv / LIST OF ILLUSTRATIONS --- p.vi / CHAPTER / Chapter ONE --- INTRODUCTION --- p.1 / Chapter TWO --- THE LITERATURE REVIEW --- p.6 / The Family of the ARFIMA Process / Parameter Estimation of the ARFIMA Process / Applications in Economic and Financial Time Series / Chapter THREE --- THEORETICAL MODELS AND METHODOLOGY --- p.16 / Theoretical Models of Long-memory Process / Parameter Estimation / Model Selection Criteria / Hypothesis Testing / Diagnostic Checking / Evaluating the Forecasting Performance / Chapter FOUR --- EMPIRICAL RESULTS OF SIMULATION EXPERIMENTS --- p.37 / Monte Carlo Simulation / Parameter Estimation / Results of Simulation Experiments / Chapter FIVE --- DATA AND EMPIRICAL RESULTS --- p.46 / Data Description / A Long-memory Model for the Return Series / Model Evaluation / Chapter SIX --- CONCLUSION --- p.55 / TABLES --- p.58 / ILLUSTRATIONS --- p.67 / APPENDICES --- p.79 / BIBLOGRAPHY --- p.83

Several statistical models have been developed in educational measurement to determine and track the performance of students in longitudinal studies. An example of a model designed for such purpose is the latent transition analysis (LTA) model. The LTA model (Graham, Collins, Wugalter, Chung, & Hansen 1991) is a type of autoregressive model specifically designed to model transitions between class membership from Time t to Time t+1. The model however makes no assumption of ordering of the latent statuses and the transition probabilities. This project extends the LTA model by using the ordering technique proposed by Croon (1990) to introduce inequality constraints on the response probabilities of the LTA model. This new approach, referred to as the ordered latent transition analysis (OLTA) model, ensures ordering of the students' learning levels (known as statuses under LTA), and the transition probabilities. Simulation study was conducted in order to determine the adequacy of parameter recovery by OLTA as well as to evaluate the performance of the information criterion (AIC and BIC) in selecting the appropriate number of levels in the model. The simulation results showed good parameter recovery overall. Additionally, the AIC and BIC performed comparably well in selecting the correct transition model, but the AIC outperformed the BIC for the selection of optimal number of levels. An example of OLTA analysis of empirical data on reading skill development is presented.

Tang Him John. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 76-80). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgment --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 1.1.1 --- Linear Time Series --- p.2 / Chapter 1.1.2 --- Mixture Models --- p.3 / Chapter 1.1.3 --- EM algorithm --- p.6 / Chapter 1.1.4 --- Model Selection --- p.6 / Chapter 1.2 --- Main Objectives --- p.7 / Chapter 1.3 --- Outline of this thesis --- p.7 / Chapter 2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- "AR, MA, and ARMA" --- p.10 / Chapter 2.1.2 --- Stationarity --- p.11 / Chapter 2.1.3 --- ARCH and GARCH --- p.12 / Chapter 2.1.4 --- Gaussian mixture --- p.13 / Chapter 2.1.5 --- EM and GEM algorithms --- p.14 / Chapter 2.2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.16 / Chapter 2.3 --- Estimation of Gaussian mixture ARMA-GARCH model --- p.17 / Chapter 2.3.1 --- Autocorrelation and Stationarity --- p.20 / Chapter 2.3.2 --- Model Selection --- p.24 / Chapter 2.4 --- Experiments: First Step Prediction --- p.26 / Chapter 2.5 --- Chapter Summary --- p.28 / Chapter 2.6 --- Notations and Terminologies --- p.30 / Chapter 2.6.1 --- White Noise Time Series --- p.30 / Chapter 2.6.2 --- Lag Operator --- p.30 / Chapter 2.6.3 --- Covariance Stationarity --- p.31 / Chapter 2.6.4 --- Wold's Theorem --- p.31 / Chapter 2.6.5 --- Multivariate Gaussian Density function --- p.32 / Chapter 3 --- Finite Mixture-of-Expert ARMA-GARCH Model --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.1.1 --- Mixture-of-Expert --- p.34 / Chapter 3.1.2 --- Alternative Mixture-of-Expert --- p.35 / Chapter 3.2 --- ARMA-GARCH Finite Mixture-of-Expert Model --- p.36 / Chapter 3.3 --- Estimation of Mixture-of-Expert ARMA-GARCH Model --- p.37 / Chapter 3.3.1 --- Model Selection --- p.38 / Chapter 3.4 --- Experiments: First Step Prediction --- p.41 / Chapter 3.5 --- Second Step and Third Step Prediction --- p.44 / Chapter 3.5.1 --- Calculating Second Step Prediction --- p.44 / Chapter 3.5.2 --- Calculating Third Step Prediction --- p.45 / Chapter 3.5.3 --- Experiments: Second Step and Third Step Prediction . --- p.46 / Chapter 3.6 --- Comparison with Other Models --- p.50 / Chapter 3.7 --- Chapter Summary --- p.57 / Chapter 4 --- Stable Estimation Algorithms --- p.58 / Chapter 4.1 --- Stable AR(1) estimation algorithm --- p.59 / Chapter 4.2 --- Stable AR(2) Estimation Algorithm --- p.60 / Chapter 4.2.1 --- Real p1 and p2 --- p.61 / Chapter 4.2.2 --- Complex p1 and p2 --- p.61 / Chapter 4.2.3 --- Experiments for AR(2) --- p.63 / Chapter 4.3 --- Experiment with Real Data --- p.64 / Chapter 4.4 --- Chapter Summary --- p.65 / Chapter 5 --- Conclusion --- p.66 / Chapter 5.1 --- Further Research --- p.69 / Chapter A --- Equation Derivation --- p.70 / Chapter A.1 --- First Derivatives for Gaussian Mixture ARMA-GARCH Esti- mation --- p.70 / Chapter A.2 --- First Derivatives for Mixture-of-Expert ARMA-GARCH Esti- mation --- p.71 / Chapter A.3 --- First Derivatives for BYY Harmony Function --- p.72 / Chapter A.4 --- First Derivatives for stable estimation algorithms --- p.73 / Chapter A.4.1 --- AR(1) --- p.74 / Chapter A.4.2 --- AR(2) --- p.74 / Bibliography --- p.80

Chen Haiqiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 33-35). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- The Model --- p.4 / Chapter 3. --- Least Squares Estimations --- p.6 / Chapter 4. --- Inference --- p.7 / Chapter 4.1 --- Asymptotic Joint Distribution of the Threshold Estimators --- p.7 / Chapter 4.2 --- Testing Threshold Effect: Model Selection Followed by Testing --- p.13 / Chapter 5. --- Modeling --- p.16 / Chapter 5.1 --- Generic Consistency of the Threshold Estimators under specification errors --- p.17 / Chapter 5.2 --- Modeling Procedure --- p.20 / Chapter 6. --- Monte Carlo Simulations --- p.21 / Chapter 7. --- Empirical Application in the Financial Market --- p.24 / Chapter 7.1 --- Data Description --- p.26 / Chapter 7.2 --- Estimated Results --- p.26 / Chapter 8. --- Conclusion --- p.30 / References --- p.33 / Appendix 1: Proof of theorem1 --- p.36 / Appendix 2: Proof of theorem2 --- p.39 / Appendix 3: Proof of theorem3 --- p.43 / List of Graph --- p.49

This thesis assesses the usefulness of confidence indicators for short term forecasting of the economic activity in the Czech Republic. The predictive power of both the business confidence indicator and the customer confidence indicator is examined using two empirical approaches. First we predict the likelihood of economic downturn defined as a discrete event using logit models, later we estimate GDP growth out of sample forecasts in the framework of vector autoregression models. The results obtained from the downturn probability models confirm the ability of confidence indicators (especially the business confidence indicator) to estimate the current economic situation and to anticipate economic downturn one quarter ahead. Results from the out-of-sample GDP growth value forecasting are ambiguous. Nevertheless the customer confidence indicator significantly improved original forecasts based on a model with standard macroeconomic variables and therefore we conclude in favour of its predictive power. This result was indirectly confirmed by OECD as the Czech customer confidence indicator has been included as a new component in the OECD domestic composite leading indicator since April 2012.

This thesis investigates the role of the exchange rate and interest rate channels in the monetary transmission mechanism in Ukraine. The responses on the domes- tic as well as Russian economy shocks are estimated using the Vector Autoregression Model with block-exogeneity restriction. Monetary transmission did not prove to be strongly effective via neither of the estimated channels, although the exchange rate channel demonstrates the results which are more in line with the economic theory. In addition, the exchange rate channel shows the higher and more significant pass through. Further, we estimate the importance of the shocks of both home and for- eign economies for the domestic variables deviations using variance decomposition technique. The relevance of the Russian shocks in fluctuations of home variables is found out. The current estimation of the transmission mechanism is relevant due to the planned inflation targeting regime implementation in Ukraine which requires understanding of that processes in the economy. 1

Abstract not available

Dynamische Zusammenhänge zwischen den internationalen Kapitelmärkten sind seit Anfang 90-er Jahre erforscht worden. Die meisten dieser Untersuchungen betrafen dieUSA und die anderen entwickelnden Märkte. Es gibt nur wenige Untersuchungen zu diesem Thema in den sich entwickelnden Märkten. Mit Hilfe von vektorautoregressiven(VAR) Modellen überprüft diese Arbeit den dynamischen Zusammenhang zwischen den Börsen der Region Pazifisches Becken vor und nach der Asiatischen Krise 1997.Unsere Studie zeigt, dass alle Börsen in der Region Asien-Pazifik mit den anderen Börsen statistisch zusammenhängen, mit Ausnahme von China. Nach der Asiatischen Krise 1997 wurden die Märkte noch stärker integriert. Die Asiatische Krise hatte einen weltweiten Einfluß auf alle Börsen der Region Pazifisches Becken: Die Verbindungen zwischen den Börsen nach der Krise sind stärker als vor der Krise. Die Märkte, die zueinander geographisch und ökonomisch nahe liegen, haben deutlich stärkere Wechselbeziehungen.Das Ergebnis zeigt, dass der USA-Markt nicht der einzige dominierende Markt in der Region ist. Die Studie stellt fest, dass die anderen entwickelten Märkte wie z.B.Neuseeland, Australien, Hongkong und Singapur, weitere vorherrschende Märkte neben USA sind. Ein Schock in einem Markt wird schnell zu den anderen Märkten übertragen. Schocks in den sich entwickelnden Märkten werden zu anderen Märkten schnell übertragen, aber ohne einen solchen großen Einfluß wie die aus den entwickelten Märkten. / International capital markets linkages have been studied since the early 90-es. Most of these studies have focused on the US and other developed markets. There are only few researches on this topic in the emerging markets. This paper examines the dynamic linkages between Pacific-Basin stock markets before and after the Asian Crisis 1997, using the vector autoregressive (VAR) approach. Our study shows that all Asia-Pacific stock markets are integrated with each other,except China. Following Asian Crisis 1997, the markets became more integrated. The Asian crisis had a global effect on all stock markets in Pacific-Basin region. The linkages between stock markets after the crisis are stronger than those before the crisis. Markets that are geographically and economically closer to each other tend to have a stronger correlation The result shows that the US market is not the only dominant market in the region. The study notes that the other developed markets such as New Zealand, Australia, Hongkong and Singapore are further comparatively dominant markets besides US market. A shock in one market is rapidly transmitted to other markets. Shocks in the emerging markets are also swiftly passed to other markets, but without having such a big effect compared to those in the developed markets.

One of the key steps of the liberalization of the Turkish electricity market has been the estab- lishment of PMUM (Turkish day ahead electricity market). The aim of this study is to explore the dynamics of electricity prices observed in this market and their relation with temperature observed in Turkey. The electricity price process is studied as a univariate process and the same process is studied along with temperature together as a two-dimensional process. We give a fairly complete model of temperature. We observe that the electricity prices in Turkey exhibit many of the features that similar prices exhibit in other world markets. In particular, Turkish day ahead prices are seasonal / every year the price seems to follow a path similar to the one years preceding it. To simplify our analysis we focus our study to a 35 day pe- riod where every year the prices show a relatively simple behavior. We study the effects of the fluctuations in temperature in this period on the fluctutations in the day ahead electricity price.