Jeong, Jinook (2006): Bootstrap Tests Based on GoodnessofFit Measures for Nonnested Hypotheses in Regression Models.

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Abstract
This paper utilizes the bootstrap to construct tests using the measures for goodnessoffit for nonnested regression models. The bootstrap enables us to compute the statistical significance of the differences in the measures and to formally test on nonnested regression models. The bootstrap tests that this paper proposes are expected to show better finite sample properties since they do not have accumulated errors in the computation process. Moreover, the bootstrap tests remove the possibility of inconsistent test results that the previous tests suffer from. Because the bootstrap tests only evaluate if a model has a significantly higher explanatory power than the other model, there is no possibility for inconsistent results. This study presents Monte Carlo simulation results to compare the finite sample properties of the proposed tests with the previous tests such as Cox test and Jtest.
Item Type:  MPRA Paper 

Original Title:  Bootstrap Tests Based on GoodnessofFit Measures for Nonnested Hypotheses in Regression Models 
Language:  English 
Keywords:  nonnested regression models, bootstrap, goodnessoffit measures 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General 
Item ID:  9789 
Depositing User:  Jinook Jeong 
Date Deposited:  01 Aug 2008 11:27 
Last Modified:  02 Oct 2019 16:59 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/9789 