Jackknife Inference for Fixed Effects Models

TL;DR

This paper introduces an automatic, computationally efficient, tuning-free jackknife inference method for fixed effects models that is highly adaptable across different models.

Contribution

It develops a novel jackknife-based inference approach that is automatic, inexpensive, tuning-free, and model agnostic for fixed effects models.

Findings

Provides a new jackknife $t$-statistic for hypothesis testing.

Enables easy construction of confidence intervals and p-values.

Applicable to a wide range of fixed effects models.

Abstract

This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, (iii) tuning parameter-free, and (iv) highly model agnostic. Specifically, we show how to combine a collection of subsample estimators into a jackknife $t$-statistic, from which hypothesis tests, confidence intervals, and $p$-values are readily obtained.