February 01 2004 Functional Restriction and Efficiency in Causal Inference Jinyong Hahn Jinyong Hahn UCLA Search for other works by this author on: This Site Google Scholar Author and Article Information Jinyong Hahn UCLA Received: July 11 2001 Accepted: February 13 2003 Online Issn: 1530-9142 Print Issn: 0034-6535 © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology2004 The Review of Economics and Statistics (2004) 86 (1): 73–76. https://doi.org/10.1162/003465304323023688 Article history Received: July 11 2001 Accepted: February 13 2003 Cite Icon Cite Permissions Share Icon Share MailTo Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Jinyong Hahn; Functional Restriction and Efficiency in Causal Inference. The Review of Economics and Statistics 2004; 86 (1): 73–76. doi: https://doi.org/10.1162/003465304323023688 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThe Review of Economics and Statistics Search Advanced Search This content is only available as a PDF. © 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology2004 Article PDF first page preview Close Modal You do not currently have access to this content.

Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications. We show that if T grows at the same rate as n, the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife. We show T growing faster than n-super-1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife. Copyright The Econometric Society 2004.

The problem of when to control for continuous or high-dimensional discrete covariate vectors arises in both experimental and observational studies. Large-cell asymptotic arguments suggest that full control for covariates or stratification variables is always efficient, even if treatment is assigned independently of covariates or strata. Here, we approximate the behavior of different estimators using a panel-data-type asymptotic sequence with fixed cell sizes and the number of cells increasing to infinity. Exact calculations in simple examples and Monte Carlo evidence suggest this generates a substantially improved approximation to actual finite-sample distributions. Under this sequence, full control for covariates is dominated by propensity-score matching when cell sizes are small, the explanatory power of the covariates conditional on the propensity score is low, and/or the probability of treatment is close to 0 or 1. Our panel-asymptotic framework also provides an explanation for why propensity-score matching can dominate covariate matching even when there are no empty cells. Finally, we introduce a random-effects estimator that provides finite-sample efficiency gains over both covariate matching and propensity-score matching.