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Hypothesis testing, specification testing, and model selection based on the MCMC output using R. (English) Zbl 1439.62072
Vinod, Hrishikesh D. (ed.) et al., Conceptual econometrics using R. Amsterdam: Elsevier/North Holland. Handb. Stat. 41, 81-115 (2019).
Summary: This chapter overviews several MCMC-based test statistics for hypothesis testing and specification testing and MCMC-based model selection criteria developed in recent years. The statistics for hypothesis testing can be viewed as the MCMC version of the “trinity” of test statistics based in maximum likelihood (ML), namely, the likelihood ratio (LR) test, the Lagrange multiplier (LM) test, and the Wald test. The model selection criteria correspond to two predictive distributions. One of them can be viewed as the MCMC version of widely used information criterion, AIC. The asymptotic distributions of the test statistics and model selection criteria are discussed. The test statistics and model selection criteria are applied to several popular models using real data, one of which involves latent variables. The implementation is illustrated in R with the MCMC output obtained by R2WinBUGS.
For the entire collection see [Zbl 1430.62013].
62F03 Parametric hypothesis testing
62F07 Statistical ranking and selection procedures
62B10 Statistical aspects of information-theoretic topics
65C05 Monte Carlo methods
62P20 Applications of statistics to economics
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