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WORKING PAPER
Bootstrap Methods for Non-nested Hypothesis Tests
Mebane, Walter R.Sekhon, Jasjeet Abstract Cox (1961; 1962) proposed a fairly general method that can be used to construct powerful tests of alternative hypotheses from separate statistical families. We prove that non-parametric bootstrap methods can produce consistent and second-order correct approximations to the distribution of the Cox statistic for non-nested LISREL-style covariance structure models. We use the method to investigate a question about the specification of a LISREL model used by Kinder, Adams and Gronke (1989). In a second application---a pair of non-nested endogenous switching regression models with tobit-style censoring, applied to real data---we illustrate how bootstrap calibration can be used to correct the size of the test when the test distribution is being estimated by Monte Carlo simulation due to concern about nonregularity. Keywords Bootstrap Cox Test Endogenous Switching Regression LISREL Tobit-Style Censoring File
Uploaded 07-20-1996 Document ID Number 368 |
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