Workbook from Structural Breaks and Switching Models course
Posted: Wed Sep 28, 2011 3:43 pmThis is the fourth in our collection of course materials. It treats a broad range of subjects, including tests for structural breaks and threshold effects, and estimation of threshold autoregression (TAR) and smooth transition (STAR) models. More than half the course is devoted to the topic of Markov Switching models, with applications to regressions, VAR, State-Space, and ARCH and GARCH models. It covers both maximum likelihood (EM where appropriate) and Bayesian estimation techniques.
The course includes a 229 page book in PDF form, with 36 executable programs with data. (Note: some of the examples require at least version 7.3 of RATS). The examples are the heart of the course. The book includes enough theory to make sense of the examples, but mainly explains in quite a bit of detail the calculations done in the examples.
The price for the course materials is $50. To order, go to https://www.estima.com/shopcart/webordercart_courses.shtml.
Table of Contents of Workbook (examples are in bold)
Preface
1 Estimation with Breaks at Known Locations
1.1 Breaks in Static Models
1.2 Breaks in Dynamic Models
1.3 RATS Tips and Tricks
2. Fluctuation Tests
2.1 Simple Fluctuation Test
2.2 Fluctuation Test for GARCH
3 Parametric Tests
3.1 LM Tests
3.1.1 Full Coefficient Vector
3.1.2 Outliers and Shifts
3.1 Break Analysis for GMM
3.2 ARIMA Model with Outlier Handling
3.3 GARCH Model with Outlier Handling
4 TAR Models
4.1 Estimation
4.2 Testing
4.2.1 Arranged Autoregression Test
4.2.2 Fixed Regressor Bootstrap
4.3 Forecasting
4.4 Generalized Impulse Responses
4.1 TAR Model for Unemployment
4.2 TAR Model for Interest Rate Spread
5 Threshold VAR/Cointegration
5.1 Threshold Error Correction
5.2 Threshold VAR
5.3 Threshold Cointegration
5.1 Threshold Error Correction Model
5.2 Threshold Error Correction Model: Forecasting
5.3 Threshold VAR
6 STAR Models
6.1 Testing for STAR
6.1 LSTAR Model: Testing and Estimation
6.2 LSTAR Model: Impulse Responses
7 Mixture Models
7.1 Maximum Likelihood
7.2 EM Estimation
7.3 Bayesian MCMC
7.3.1 Label Switching
7.1 Mixture Model-Maximum Likelihood
7.2 Mixture Model-EM
7.3 Mixture Model-MCMC
8 Markov Switching: Introduction
8.1 Common Concepts
8.1.1 Prediction Step
8.1.2 Update Step
8.1.3 Smoothing
8.1.4 Simulation of Regimes
8.1.5 Pre-Sample Regime Probabilities
8.2 Estimation
8.2.1 Simple Example
8.2.2 Maximum Likelihood
8.2.3 EM
8.2.4 MCMC (Gibbs Sampling)
8.1 Markov Switching Variances-ML
8.2 Markov Switching Variances-EM
8.3 Markov Switching Variances-MCMC
9 Markov Switching Regressions
9.1 Estimation
9.1.1 MSREGRESSION procedures
9.1.2 The example
9.1.3 Maximum Likelihood
9.1.4 EM
9.1.5 MCMC (Gibbs Sampling)
9.1 MS Linear Regression: ML Estimation
9.2 MS Linear Regression: EM Estimation
9.3 MS Linear Regression: MCMC Estimation
10 Markov Switching Multivariate Regressions
10.1 Estimation
10.1.1 MSSYSREGRESSION procedures
10.1.2 The example
10.1.3 Maximum Likelihood
10.1.4 EM
10.1.5 MCMC (Gibbs Sampling)
10.2 Systems Regression with Fixed Coefficients
10.1 MS Systems Regression: ML Estimation
10.2 MS Systems Regression: EM Estimation
10.3 MS Systems Regression: MCMC Estimation
11 Markov Switching VAR’s
11.1 Estimation
11.1.1 The example
11.1.2 MSVARSETUP procedures
11.1.3 Maximum Likelihood
11.1.4 EM
11.1.5 MCMC (Gibbs Sampling)
11.1 Hamilton Model: ML Estimation
11.2 Hamilton Model: EM Estimation
11.3 Hamilton Model: MCMC Estimation
12 Markov Switching State-Space Models
12.1 The Examples
12.2 The Kim Filter
12.2.1 Lam Model by Kim Filter
12.2.2 Time-Varying Parameters Model by Kim Filter
12.3 Estimation with MCMC
12.3.1 Lam Model by MCMC
12.3.2 Time-varying parameters by MCMC
12.1 Lam GNP Model-Kim Filter
12.2 Time-Varying Parameters-Kim Filter
12.3 Lam GNP Model-MCMC
12.4 Time-Varying Parameters-MCMC
13 Markov Switching ARCH and GARCH
13.1 Markov Switching ARCH models
13.1.1 Estimation by ML
13.1.2 Estimation by MCMC
13.2 Markov Switching GARCH
13.1 MS ARCH Model-Maximum Likelihood
13.2 MS ARCH Model-MCMC
13.3 MS GARCH Model-Approximate ML
Appendices
A A General Result on Smoothing
B The EM Algorithm
C Hierarchical Priors
D Gibbs Sampling and Markov Chain Monte Carlo
E Probability Distributions
E.1 Univariate Normal
E.2 Beta distribution
E.3 Gamma Distribution
E.4 Inverse Gamma Distribution
E.5 Bernoulli Distribution
E.6 Multivariate Normal
E.7 Dirichlet distribution
E.8 Wishart Distribution
Bibliography
Index
The course includes a 229 page book in PDF form, with 36 executable programs with data. (Note: some of the examples require at least version 7.3 of RATS). The examples are the heart of the course. The book includes enough theory to make sense of the examples, but mainly explains in quite a bit of detail the calculations done in the examples.
The price for the course materials is $50. To order, go to https://www.estima.com/shopcart/webordercart_courses.shtml.
Structural Breaks Sample Chapter.pdf- Sample chapter
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Table of Contents of Workbook (examples are in bold)
Preface
1 Estimation with Breaks at Known Locations
1.1 Breaks in Static Models
1.2 Breaks in Dynamic Models
1.3 RATS Tips and Tricks
2. Fluctuation Tests
2.1 Simple Fluctuation Test
2.2 Fluctuation Test for GARCH
3 Parametric Tests
3.1 LM Tests
3.1.1 Full Coefficient Vector
3.1.2 Outliers and Shifts
3.1 Break Analysis for GMM
3.2 ARIMA Model with Outlier Handling
3.3 GARCH Model with Outlier Handling
4 TAR Models
4.1 Estimation
4.2 Testing
4.2.1 Arranged Autoregression Test
4.2.2 Fixed Regressor Bootstrap
4.3 Forecasting
4.4 Generalized Impulse Responses
4.1 TAR Model for Unemployment
4.2 TAR Model for Interest Rate Spread
5 Threshold VAR/Cointegration
5.1 Threshold Error Correction
5.2 Threshold VAR
5.3 Threshold Cointegration
5.1 Threshold Error Correction Model
5.2 Threshold Error Correction Model: Forecasting
5.3 Threshold VAR
6 STAR Models
6.1 Testing for STAR
6.1 LSTAR Model: Testing and Estimation
6.2 LSTAR Model: Impulse Responses
7 Mixture Models
7.1 Maximum Likelihood
7.2 EM Estimation
7.3 Bayesian MCMC
7.3.1 Label Switching
7.1 Mixture Model-Maximum Likelihood
7.2 Mixture Model-EM
7.3 Mixture Model-MCMC
8 Markov Switching: Introduction
8.1 Common Concepts
8.1.1 Prediction Step
8.1.2 Update Step
8.1.3 Smoothing
8.1.4 Simulation of Regimes
8.1.5 Pre-Sample Regime Probabilities
8.2 Estimation
8.2.1 Simple Example
8.2.2 Maximum Likelihood
8.2.3 EM
8.2.4 MCMC (Gibbs Sampling)
8.1 Markov Switching Variances-ML
8.2 Markov Switching Variances-EM
8.3 Markov Switching Variances-MCMC
9 Markov Switching Regressions
9.1 Estimation
9.1.1 MSREGRESSION procedures
9.1.2 The example
9.1.3 Maximum Likelihood
9.1.4 EM
9.1.5 MCMC (Gibbs Sampling)
9.1 MS Linear Regression: ML Estimation
9.2 MS Linear Regression: EM Estimation
9.3 MS Linear Regression: MCMC Estimation
10 Markov Switching Multivariate Regressions
10.1 Estimation
10.1.1 MSSYSREGRESSION procedures
10.1.2 The example
10.1.3 Maximum Likelihood
10.1.4 EM
10.1.5 MCMC (Gibbs Sampling)
10.2 Systems Regression with Fixed Coefficients
10.1 MS Systems Regression: ML Estimation
10.2 MS Systems Regression: EM Estimation
10.3 MS Systems Regression: MCMC Estimation
11 Markov Switching VAR’s
11.1 Estimation
11.1.1 The example
11.1.2 MSVARSETUP procedures
11.1.3 Maximum Likelihood
11.1.4 EM
11.1.5 MCMC (Gibbs Sampling)
11.1 Hamilton Model: ML Estimation
11.2 Hamilton Model: EM Estimation
11.3 Hamilton Model: MCMC Estimation
12 Markov Switching State-Space Models
12.1 The Examples
12.2 The Kim Filter
12.2.1 Lam Model by Kim Filter
12.2.2 Time-Varying Parameters Model by Kim Filter
12.3 Estimation with MCMC
12.3.1 Lam Model by MCMC
12.3.2 Time-varying parameters by MCMC
12.1 Lam GNP Model-Kim Filter
12.2 Time-Varying Parameters-Kim Filter
12.3 Lam GNP Model-MCMC
12.4 Time-Varying Parameters-MCMC
13 Markov Switching ARCH and GARCH
13.1 Markov Switching ARCH models
13.1.1 Estimation by ML
13.1.2 Estimation by MCMC
13.2 Markov Switching GARCH
13.1 MS ARCH Model-Maximum Likelihood
13.2 MS ARCH Model-MCMC
13.3 MS GARCH Model-Approximate ML
Appendices
A A General Result on Smoothing
B The EM Algorithm
C Hierarchical Priors
D Gibbs Sampling and Markov Chain Monte Carlo
E Probability Distributions
E.1 Univariate Normal
E.2 Beta distribution
E.3 Gamma Distribution
E.4 Inverse Gamma Distribution
E.5 Bernoulli Distribution
E.6 Multivariate Normal
E.7 Dirichlet distribution
E.8 Wishart Distribution
Bibliography
Index