Modern computer systems are now so powerful that they can be used to carry out
simulation-based statistical investigations without involving delays or the need to access
high levels of equipment. When carrying out econometric analyses, the routine use of
computer-based methods offers a valuable alternative to the standard approach in which
approximations are based upon what happens as the sample size grows without limit. Applied
work has to be based upon a finite number of observations. Computationally-intensive
techniques and, in particular, bootstrap methods provide ways to improve the finite-sample
performance of well-known tests. Bootstrap tests can also be employed when conventional
theory does not lead to a test statistic, which can be compared with critical values from
some standard distribution. This book uses the familiar linear regression model as a
framework for introducing simulation-based tests to applied workers, students and others
who carry out empirical econometric analyses.
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 Statistics
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
344 pages, Ppaerback
