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

Shunan Zhao, Ruiqi Liu, Zuofeng Shang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)325-337
Number of pages13
JournalJournal of Business and Economic Statistics
Issue number1
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


  • Empirical process; Functional Bahadur representation; Interactive fixed effect; Joint asymptotic distribution


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