State-space models with regime switching : classical and Gibbs-sampling approaches with applications /
| Основен автор: | Kim, Chang-Jin, 1960- |
|---|---|
| Други автори: | Nelson, Charles R. |
| Формат: | Електронна книга |
| Език: | English |
| Публикувано: |
Cambridge, Mass. :
MIT Press,
℗♭1999.
|
| Предмети: | |
| Онлайн достъп: |
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=9231 |
| Подобни документи: |
Print version::
State-space models with regime switching. |
Съдържание:
- State-Space Models and Markov Switching in Econometrics: A Brief History
- Computer Programs and Data
- 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
- 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
- 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
- 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.