Bootstrap Tests for Regression Models

Paperback | September 15, 2009

byLeslie Godfrey

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This book contains 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.

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This book contains 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 ...

LESLIE GODFREY is Professor of Econometrics at the University of York, UK and a Fellow of the Journal of Econometrics. He has served on the editorial boards of Econometric Theory and Econometric Reviews. His articles have been published in leading journals, including Econometrica, Journal of Econometrics and Review of Economics and St...

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Format:PaperbackDimensions:344 pages, 8.95 × 5.57 × 0.8 inPublished:September 15, 2009Publisher:Palgrave MacmillanLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0230202314

ISBN - 13:9780230202313

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Table of Contents

Preface
PART I: TESTS FOR LINEAR REGRESSION MODELS
Introduction
Tests for the Classical Linear Regression Model
Tests for Linear Regression Models Under Weaker Assumptions: Random Regressors and Non-Normal IID Errors
Tests for Generalized Linear Regression Models
Finite-Sample Properties of Asymptotic Tests 
Non-Standard Tests for Linear Regression Models
Summary and Concluding Remarks
PART II: SIMULATION-BASED TESTS: BASIC IDEAS
Introduction
Some Simple Examples of Tests for IID Variables and Key Concepts
Simulation-Based Tests for Regression Models
Asymptotic Properties of Bootstrap Tests
The Double Bootstrap
Summary and Concluding Remarks
PART III: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME STANDARD CASES
Introduction
A Monte Carlo Test of the Assumption of Normality
Simulation-Based Tests for Heteroskedasticity
Bootstrapping F Tests of Linear Coefficient Restrictions
Bootstrapping LM Tests for Serial Correlation in Dynamic Regression Models
Summary and Concluding Remarks
PART IV: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME NON-STANDARD CASES
Introduction
Bootstrapping Predictive Tests
Using Bootstrap Methods with a Battery of OLS Diagnostic Tests
Bootstrapping Tests for Structural Breaks
Summary and Conclusions
PART V: BOOTSTRAP METHODS FOR REGRESSION MODELS WITH NON-IID ERRORS
Introduction
Bootstrap Methods for Independent Heteroskedastic Errors
Bootstrap Methods for Homoskedastic Autocorrelated Errors
Bootstrap Methods for Heteroskedastic Autocorrelated Errors
Summary and Concluding Remarks
PART VI: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH NON-IID ERRORS
Introduction
Bootstrapping Heteroskedasticity-Robust Regression Specification Error Tests
Bootstrapping Heteroskedasticity-Robust Autocorrelation Tests for Dynamic
Models
Bootstrapping Heteroskedasticity-Robust Structural Break Tests with an Unknown Breakpoint
Bootstrapping Autocorrelation-Robust Hausman Tests
Summary and Conclusions
PART VII:
Simulation-Based Tests for Non-Nested Regression Models
Introduction
Asymptotic Tests for Models with Non-Nested Regressors
Bootstrapping Tests for Models with Non-Nested Regressors
Bootstrapping the LLR Statistic with Non-Nested Models
Summary and Concluding Remarks
PART VIII: EPILOGUE
Bibliography
Author Index
Subject Index