Tests for linear regression models -- Simulation-based tests : basic ideas -- Simulation-based tests for regression models with IID errors : some standard cases -- Simulation-based tests for regression models with IID errors : some non-standard cases -- Bootstrap methods for regression models with non-IID errors -- Simulation-based tests for regression models with non-IID errors -- Simulation-Based Tests for Non-Nested Regression Models
Summary
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
Analysis
Bootstrap (Statistics)
Econometrics
Regression analysis
Bibliography
Includes bibliographical references (pages 305-317) and indexes
Notes
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