Jackknife Inference for Fixed Effects Models

2026-02-25

AI Summary

This study addresses the lack of an automatic, efficient, and model-agnostic inference procedure for fixed-effects models. The authors propose a general inference framework that constructs a self-normalized jackknife t-statistic from subsample estimators, enabling direct computation of hypothesis tests, confidence intervals, and p-values. The method requires no complex tuning parameters, offers high computational efficiency, and exhibits strong robustness to model specification. Applicable across a broad class of fixed-effects models, this approach substantially streamlines statistical inference and facilitates automated, unified inferential practice.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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

Problem

• jackknife
• fixed effects
• inference
• model agnostic
• self-normalised

Innovation

• jackknife
• fixed effects
• self-normalized
• subsample estimation
• model-agnostic inference