Spelling suggestions: "subject:"autoregression (dtatistics)"" "subject:"autoregression (estatistics)""
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An examination of stock market properties vector autoregression approach /Jeon, Kyung-Seong, January 1997 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 147-152). Also available on the Internet.
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Nonparametric bayesian density estimation with intrinsic autoregressive priors /Lee, Suhwon, January 2003 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Leaves 14 and 46 are blank. Typescript. Vita. Includes bibliographical references (leaves 96-100). Also available on the Internet.
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Nonparametric bayesian density estimation with intrinsic autoregressive priorsLee, Suhwon, January 2003 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Leaves 14 and 46 are blank. Typescript. Vita. Includes bibliographical references (leaves 96-100). Also available on the Internet.
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Optimal asset allocation under GARCH model /Hui, Wai-choi. January 2000 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 87-91).
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Bayesian nonparametric methods for some econometric problems /Lau, Wai Kwong. January 2005 (has links)
Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 89-92). Also available in electronic version.
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The combination of high and low frequency data in macroeconometric forecasts: the case of Hong Kong.January 1999 (has links)
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
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An analysis of the Hong Kong stock market by the ARFIMA-GARCH model.January 2001 (has links)
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
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The Ordered Latent Transition Analysis Model for the Measurement of LearningNsowaa, Bright January 2018 (has links)
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.
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Finite Gaussian mixture and finite mixture-of-expert ARMA-GARCH models for stock price prediction.January 2003 (has links)
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
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Threshold autoregressive model with multiple threshold variables.January 2005 (has links)
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
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