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.
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
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