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| Preface and Acknowledgments | |
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| Introduction | |
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| State-Space Models and Markov Switching in Econometrics: A Brief History | |
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| Computer Programs and Data | |
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| References | |
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| The Classical Approach | |
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| The Maximum Likelihood Estimation Method: Practical Issues | |
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| Maximum Likelihood Estimation and the Covariance Matrix of OML | |
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| The Prediction Error Decomposition and the Likelihood Function | |
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| Parameter Constraints and the Covariance Matrix of OML | |
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| References | |
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| State-Space Models and the Kalman Filter | |
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| Time-Varying-Parameter Models and the Kalman Filter | |
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| State-Space Models and the Kalman Filter | |
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| Application 1: A Decomposition of Real GDP and the Unemployment Rate into Stochastic Trend and Transitory Components | |
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| Application 2: An Application of the Time-Varying-Parameter Model to Modeling Changing Conditional Variance | |
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| Application 3: Stock and Watson's Dynamic Factor Model of the Coincident Economic Indicators | |
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| GAUSS Programs to Accompany Chapter 3 | |
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| References | |
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| Markov-Switching Models | |
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| Introduction: Serially Uncorrelated Data and Switching | |
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| Serially Correlated Data and Markov Switching | |
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| Issues Related to Markov-Switching Models | |
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| Application 1: Hamilton's Markov-Switching Model of Business Fluctuations | |
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| Application 2: A Unit Root in a Three-State Markov-Switching Model of the Real Interest Rate | |
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| Application 3: A Three-State Markov-Switching Variance Model of Stock Returns | |
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| GAUSS Programs to Accompany Chapter 4 | |
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| References | |
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| State-Space Models with Markov Switching | |
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| Specification of the Model | |
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| The Basic Filter and Estimation of the Model | |
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| Smoothing | |
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| An Evaluation of the Kim Filter and Approximate MLE | |
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| Application 1: Sources of Monetary Growth Uncertainty and Economic Activity | |
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| Application 2: Friedman's Plucking Model of Business Fluctuations and Implied Business Cycle Asymmetry | |
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| Application 3: A Dynamic Factor Model with Markov Switching: Business Cycle Turning Points and a New Coincident Index | |
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| GAUSS Programs to Accompany Chapter 5 | |
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| References | |
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| State-Space Models with Heteroskedastic Disturbances | |
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| State-Space Models with ARCH Disturbances | |
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| State-Space Models with Markov-Switching Heteroskedasticity | |
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| Application 1: The Link between the Inflation Rate and Inflation Uncertainty | |
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| Application 2: Transient Fads and the Crash of '87 in the U.S. Stock Market | |
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| GAUSS Programs to Accompany Chapter 6 | |
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| References | |
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| The Gibbs-Sampling Approach | |
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| An Introduction to Bayesian Inference and Gibbs-Sampling | |
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| Classical versus Bayesian Analysis: Fundamental Differences | |
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| Bayesian Analysis: An Introduction | |
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| Gibbs-Sampling: Motivation and Basic Idea | |
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| Examples of Gibbs-Sampling in Econometrics | |
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| GAUSS Programs to Accompany Chapter 7 | |
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| References | |
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| State-Space Models and Gibbs-Sampling | |
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| Generating the State Vector When Q Is Positive-Definite | |
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| Generating the State Vector When Q Is Singular: A Generalization | |
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| Application 1: A Gibbs-Sampling Approach to a Linear Dynamic Factor Model and a New Coincident Index | |
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| GAUSS Program to Accompany Chapter 8 | |
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| References | |
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| Markov-Switching Models and Gibbs-Sampling | |
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| A Basic Model and the Bayesian Gibbs-Sampling Approach | |
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| Application 1: A Three-State Markov-Switching Variance Model of Stock Returns | |
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| Application 2: A Three-State Markov Switching Mean-Variance Model of the Real Interest Rate | |
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| GAUSS Programs to Accompany Chapter 9 | |
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| References | |
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| State-Space Models with Markov Switching and Gibbs-Sampling | |
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| General Framework | |
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| Application 1: Business Cycle Turning Points and a New Coincident Index | |
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| Application 2: Business Cycle Duration Dependence within a Dynamic Factor Model: An Advantage of the Gibbs-Sampling Approach over the Classical Approach | |
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| Application 3: An Unobserved Components Model of the Long-Run U.S./U.K. Real Exchange Rate with Heteroskedasticity | |
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| GAUSS Program to Accompany Chapter 10 | |
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| References | |
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| Gibbs-Sampling and Parameter Uncertainty: Testing for Mean Reversion in Heteroskedastic Data | |
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| Alternative Ways of Incorporating Parameter Uncertainty | |
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| Variance Ratio Tests of Mean Reversion: A Review | |
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| Historical Pattern of Heteroskedasticity and the Sampling Distribution of the Variance Ratio Statistic | |
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| New Tests of Mean Reversion in the Presence of Heteroskedasticity | |
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| GAUSS Programs to Accompany Chapter 11 | |
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| References | |
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| Index | |