Skip to content
Review

Panel Conditioning in Fixed-Effects Models: Identification and Bias Propagation

Sep 2026 · 0 citations
Mathematics Economics

Abstract

Panel conditioning, the causal effect of prior survey participation on responses, can vary with tenure. Under an additive model of cell means in period, entry cohort, and tenure, we characterize which features of the conditioning path a staggered panel identifies on its observed support, and how the unidentified component affects common panel estimators. The identified set of the path is an affine translate of the tenure projection of the cell design's kernel, and a linear functional of the path is identified exactly when it annihilates that projection. It always contains an affine direction and, when the entry cohorts share a stride, periodic directions, which exhaust it under a connectivity condition on observed increments; second differences at that stride are then identified, and ordinary ones generally are not when the stride exceeds one. Under a recruitment condition, an interrupted schedule such as the four-eight-four rotation of the Current Population Survey (CPS) distinguishes a constant increment per interview from one per calendar month, which no equally spaced schedule can. We give support conditions for recovery under a plateau, entry-wave negative controls, or bounded cohort drift. A second set of results links identification to regression: two-way fixed effects absorb every unidentified direction, so the remaining conditioning bias is normalization-invariant and itself identified, and a two-way regression with tenure indicators corrects it under a residual-rank condition. When event time is aligned with tenure, conditioning shifts event-study coefficients by a known linear functional of the path, producing pre-trends without anticipation; bounds on identified curvature bound those shifts. Simulations verify the identities, a 19-wave Japanese panel illustrates the support calculations, and published CPS month-in-sample indices give a descriptive, not identifying, example.

View source

Similar papers

Preprint Oct 2026

Identifying Panel Conditioning with Refreshment Samples: Sharp Bounds and Design Assumptions

Refreshment samples are the standard remedy for panel attrition, and the identification results behind them maintain that participation does not change measurement. We characterize what a refreshment sample identifies about panel conditioning, modelled as a deterministic monotone map at reinterview, when attrition is u...

Shoki Okubo · 0 citations
Preprint Sep 2026

Causal Inference under Dynamic Selection: Time-Varying Covariates and Latent Heterogeneity

I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible inte...

Wei-Sheng Zhang · 0 citations
Preprint Aug 2026

Moments of Random Coefficients in Short Panels

We study identification and estimation of moments of random coefficients in short linear panels, allowing the number of heterogeneous coefficients to exceed the number of equations observed for each unit. Under moment homogeneity, different regressor histories impose restrictions on the same moment vector. We give nece...

Irene Botosaru, James L. Powell · 0 citations
Preprint Aug 2026

Stationary Errors and Quantile Regression in Short Panels

This paper studies a linear panel model with an unrestricted individual effect and a time- stationary idiosyncratic disturbance. We first show that stationarity is a strong restriction in a quantile model. In a linear conditional quantile specification with quantile-dependent slopes, equality of the conditional residua...

Shakeeb Khan, E. Tamer · 0 citations
Preprint Aug 2026

Learning about Treatment Effects in Panels under Unknown Interference

When comparison units may also respond to treatment, panel comparisons reflect both the treatment effect and spillovers. If the interference pattern is unknown, observed outcomes alone do not separate the two. I characterize what can nevertheless be learned from panel outcomes under general restrictions, without requir...

Sheng-Li Wei · 0 citations
Open access Oct 2026

Endogeneity

Endogeneity is the condition in which a regressor is correlated with a model's error term, so that its coefficient does not recover the causal effect of interest. This entry generalizes the omitted-variable case of entry 008, presenting omitted variables, simultaneity, selection, and measurement error as four mechanism...

W. Craytor · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.