Bootstrap Tests for Regression Models by L. Godfrey

By L. Godfrey

An obtainable dialogue studying computationally-intensive suggestions and bootstrap equipment, supplying how one can enhance the finite-sample functionality of famous asymptotic assessments for regression versions. 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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60) in which uˆ is the n-dimensional OLS residual vector obtained when all observations are used for estimation and uˇ is the n1 -dimensional OLS residual vector when only the estimation sample is used. Under the null hypothesis that the same classical assumptions apply to all n observations, P has the F n2 , n1 − k distribution with large values of this test statistic indicating predictive failure. 60) is assessed using right-hand tail critical values of the F n2 , n1 − k distribution, the null model under test includes the assumption that the errors ui are Normally distributed.

Misspecification of the model for autocorrelation and/or heteroskedasticity will, in general, lead to an inconsistent estimator of the covariance matrix of the minimizers of QNLS (β, ψ) and QFGLS (β). 18), because such tests use the estimated covariance matrix to assess the significance of sample outcomes. An investigation of the effects of misspecifying the model for heteroskedasticity is reported in Belsley (2002). It is found that effects can be serious and Belsley concludes that Correction for heteroskedasticity clearly does best when both the proper arguments and the proper form of the skedasticity function are known.

Under the null Tests for Linear Regression Models 37 hypothesis HCC , the standard F test yields a statistic, denoted by FCC , which is distributed as F(k, n − 2k). While this textbook version of the Chow test may sometimes be applicable, it has been recognized that it is often the case that there is uncertainty about the break in coefficient values. Suppose that a researcher is using time series data to estimate a regression relationship and there is concern that there was a single change in coefficients but it is not known exactly when it occurred.

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