By L. Godfrey
An available dialogue studying computationally-intensive concepts and bootstrap tools, offering how one can enhance the finite-sample functionality of famous asymptotic exams for regression types. This e-book makes use of the linear regression version as a framework for introducing simulation-based exams to aid practice econometric analyses.
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Additional resources for Bootstrap Tests for Regression Models
It is hoped that the results obtained from simulation experiments are of value in practical situations, but this property can never be guaranteed. It is very common to refer to such experiments as Monte Carlo experiments; see Davidson and MacKinnon (1993, ch. 21) for an excellent discussion. However, given that “Monte Carlo” will often be used below to refer to a particular type of simulationbased test, this terminology will not be adopted in order to avoid any confusion. There are three sets of simulation experiments, each reported in its own subsection.
The choice can be based simply on convenience, given the set of error distributions to be used. The distributions that are used to obtain the terms of (u1 , . . , u27 ) are: N(0, 1) as a benchmark; t(5) as an example of a symmetric distribution that is far from being Normal; and the heavily-skewed χ 2 (2) distribution. Drawings from these distributions are adjusted, when necessary, so that, without loss of generality, they come from a population with zero mean and variance equal to one. ) In order to derive fairly precise estimates of rejection probabilities, 25,000 sets of artiﬁcial data with n = 27 are generated for each error distribution, that is, the number of replications is R = 25,000.
However, there is evidence from simulation experiments which indicates that these asymptotic critical values may not be accurate approximations to actual ﬁnite sample values; see, for example, Diebold and Chen (1996). The second example of a non-standard procedure is also based upon a test described in Chow (1960). As well as proposing a test of the claim that all regression coefﬁcients are constant, Chow explains how to carry out a test of the hypothesis that prediction errors have a zero mean.