Bootstrap Tests for Regression Models 1st Edition by Leslie Godfrey ISBN 0230202306 978-0230202306
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ISBN 10: 0230202306
ISBN 13: 978-0230202306
Author: Leslie Godfrey
An accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation-based tests to help perform econometric analyses.
Bootstrap Tests for Regression Models 1st Table of contents:
1 Tests for Linear Regression Models
1.1. Introduction
1.2. Tests for the classical linear regression model
1.3. Tests for linear regression models under weaker assumptions: random regressors and non-Normal I
2 Simulation-based Tests: Basic Ideas
2.1. Introduction
2.2. Some key concepts and simple examples of tests for IID variables
2.2.1. Monte Carlo tests
2.2.2. Bootstrap tests
2.3. Simulation-based tests for regression models
2.3.1. The classical Normal model
2.3.2. Models with IID errors from an unspecified distribution
2.3.3. Dynamic regression models and bootstrap schemes
2.3.4. The choice of the number of artificial samples
2.4. Asymptotic properties of bootstrap tests
2.5. The double bootstrap
2.6. Summary and concluding remarks
3 Simulation-based Tests for Regression Models with IID Errors: Some Standard Cases
3.1. Introduction
3.2. A Monte Carlo test of the assumption of Normality
3.3. Simulation-based tests for heteroskedasticity
3.3.1. Monte Carlo tests for heteroskedasticity
3.3.2. Bootstrap tests for heteroskedasticity
3.3.3. Simulation experiments and tests for heteroskedasticity
3.4. Bootstrapping F tests of linear coefficient restrictions
3.4.1. Regression models with strictly exogenous regressors
3.4.2. Stable dynamic regression models
3.4.3. Some simulation evidence concerning asymptotic and bootstrap F tests
3.5. Bootstrapping LM tests for serial correlation in dynamic regression models
3.5.1. Restricted or unrestricted estimates as parameters of bootstrap worlds
3.5.2. Some simulation evidence on the choice between restricted and unrestricted estimates
3.6. Summary and concluding remarks
4 Simulation-based Tests for Regression Models with IID Errors: Some Non-standard Cases
4.1. Introduction
4.2. Bootstrapping predictive tests
4.2.1. Asymptotic analysis for predictive test statistics
4.2.2. Single and double bootstraps for predictive tests
4.2.3. Simulation experiments and results
4.2.4. Dynamic regression models
4.3. Using bootstrap methods with a battery of OLS diagnostic tests
4.3.1. Regression models and diagnostic tests
4.3.2. Bootstrapping the minimum p-value of several diagnostic test statistics
4.3.3. Simulation experiments and results
4.4. Bootstrapping tests for structural breaks
4.4.1. Testing constant coefficients against an alternative with an unknown breakpoint
4.4.2. Simulation evidence for asymptotic and bootstrap tests
4.5. Summary and conclusions
5 Bootstrap Methods for Regression Models with Non-IID Errors
5.1. Introduction
5.2. Bootstrap methods for independent heteroskedastic errors
5.2.1. Model-based bootstraps
5.2.2. Pairs bootstraps
5.2.3. Wild bootstraps
5.2.4. Estimating function bootstraps
5.2.5. Bootstrapping dynamic regression models
5.3. Bootstrap methods for homoskedastic autocorrelated errors
5.3.1. Model-based bootstraps
5.3.2. Block bootstraps
5.3.3. Sieve bootstraps
5.3.4. Other methods
5.4. Bootstrap methods for heteroskedastic autocorrelated errors
5.4.1. Asymptotic theory tests
5.4.2. Block bootstraps
5.4.3. Other methods
5.5. Summary and concluding remarks
6 Simulation-based Tests for Regression Models with Non-IID Errors
6.1. Introduction
6.2. Bootstrapping heteroskedasticity-robust regression specification error tests
6.2.1. The forms of test statistics
6.2.2. Simulation experiments
6.3. Bootstrapping heteroskedasticity-robust autocorrelation tests for dynamic models
6.3.1. The forms of test statistics
6.3.2. Simulation experiments
6.4. Bootstrapping heteroskedasticity-robust structural break tests with an unknown breakpoint
6.5. Bootstrapping autocorrelation-robust Hausman tests
6.5.1. The forms of test statistics
6.5.2. Simulation experiments
6.6. Summary and conclusions
7 Simulation-based Tests for Non-nested Regression Models
7.1. Introduction
7.2. Asymptotic tests for models with non-nested regressors
7.2.1. Cox-type LLR tests
7.2.2. Artificial regression tests
7.2.3. Comprehensive model F-test
7.2.4. Regularity conditions and orthogonal regressors
7.2.5. Testing with multiple alternatives
7.2.6. Tests for model selection
7.2.7. Evidence from simulation experiments
7.3. Bootstrapping tests for models with non-nested regressors
7.3.1. One non-nested alternative regression model: significance levels
7.3.2. One non-nested alternative regression model: power
7.3.3. One non-nested alternative regression model: extreme cases
7.3.4. Two non-nested alternative regression models: significance levels
7.3.5. Two non-nested alternative regression models: power
7.4. Bootstrapping the LLR statistic with non-nested models
7.5. Summary and concluding remarks
8 Epilogue
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