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Exponentially tilted likelihood inference on growing dimensional unconditional moment models

Niansheng Tang, Xiaodong Yan and Puying Zhao

Journal of Econometrics, 2018, vol. 202, issue 1, 57-74

Abstract: Growing-dimensional data with likelihood function unavailable are often encountered in various fields. This paper presents a penalized exponentially tilted (PET) likelihood for variable selection and parameter estimation for growing dimensional unconditional moment models in the presence of correlation among variables and model misspecification. Under some regularity conditions, we investigate the consistent and oracle properties of the PET estimators of parameters, and show that the constrained PET likelihood ratio statistic for testing contrast hypothesis asymptotically follows the chi-squared distribution. Theoretical results reveal that the PET likelihood approach is robust to model misspecification. We study high-order asymptotic properties of the proposed PET estimators. Simulation studies are conducted to investigate the finite performance of the proposed methodologies. An example from the Boston Housing Study is illustrated.

Keywords: Growing-dimensional data analysis; Model misspecification; Moment unconditional models; Penalized exponentially tilted likelihood; Variable selection (search for similar items in EconPapers)
Date: 2018
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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Handle: RePEc:eee:econom:v:202:y:2018:i:1:p:57-74