Semiparametric model averaging prediction for dichotomous response

Fang Fang, Jialiang Li and Xiaochao Xia

Journal of Econometrics, 2022, vol. 229, issue 2, 219-245

Abstract: Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a linear combination of one-dimensional marginal score functions. The weight parameters involved in the approximation are obtained by first smoothing the nonparametric marginal scores and then applying the parametric model averaging via a maximum likelihood estimation. The proposed SMAP provides greater flexibility than parametric models while being more stable than a fully nonparametric approach. Theoretical properties are investigated in two practical scenarios: (i) covariates are conditionally independent given the response; and (ii) the conditional independence assumption does not hold. In the first scenario, we show that SMAP puts weight one to the true model and hence the model averaging estimators are consistent. In the second scenario in which a “true” model may not exist, SMAP is shown to be asymptotically optimal in the sense that its Kullback–Leibler loss is asymptotically identical to that of the best – but infeasible – model averaging estimator. Empirical evidences from simulation studies and a real data analysis are presented to support and illustrate our methods.

Keywords: Kullback–Leibler loss; Mis-specification; Model averaging; Semiparametric model; Splines basis (search for similar items in EconPapers)
JEL-codes: C14 C51 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407620303882
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:229:y:2022:i:2:p:219-245

DOI: 10.1016/j.jeconom.2020.09.008

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().