Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method

Document Type

Article

Publication Date

1-1-2021

Abstract

We propose statistical inferential procedures for nonparametric panel data models with interactive fixed effects in a kernel ridge regression framework. Compared with the traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters. The model complexity is controlled by a continuous regularization parameter which can be automatically selected by the generalized cross-validation. Based on the empirical process theory and functional analysis tools, we derive the joint asymptotic distributions for the estimators in the heterogeneous setting. These joint asymptotic results are then used to construct the confidence intervals for the regression means and the prediction intervals for future observations, both being the first provably valid intervals in literature. The marginal asymptotic normality of the functional estimators in a homogeneous setting is also obtained. Our estimators can also be readily modified and applied to other widely used semiparametric models, such as partially linear models. Simulation and real data analyses demonstrate the advantages of our method. Supplementary materials for this article are available online.

Identifier

85073927242 (Scopus)

Publication Title

Journal of Business and Economic Statistics

External Full Text Location

https://doi.org/10.1080/07350015.2019.1660176

e-ISSN

15372707

ISSN

07350015

First Page

325

Last Page

337

Issue

1

Volume

39

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