State-Space Models with Regime Switching Classical and Gibbs-Sampling Approaches with Applications

ISBN-10: 0262112388
ISBN-13: 9780262112383
Edition: 1999
List price: $75.00 Buy it from $38.69
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Description: Both state-space and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents advances in econometric methods that make feasible the estimation of models that have both  More...

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Book details

List price: $75.00
Copyright year: 1999
Publisher: MIT Press
Publication date: 5/13/1999
Binding: Hardcover
Pages: 312
Size: 6.50" wide x 9.50" long x 0.75" tall
Weight: 1.562
Language: English

Both state-space and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents advances in econometric methods that make feasible the estimation of models that have both features.

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

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