9 papers
Fixed-Effect Saturation Is Not Weak Identification: Certifying Inference under Measurement Error
Stanisław M. S. Halkiewicz
Fixed-effect saturation alone is not weak identification. In the baseline model, fixed-effect--residualized OLS is unbiased and conventional inference is asymptotically exact at ev…
Exact Inference in Fixed-Effect Regressions with Concentrated Identifying Variation
Stanisław M. S. Halkiewicz
In fixed-effect regressions with many groups, fixed effects can absorb most identifying variation, leaving a handful of observations to carry what remains. When variation is this c…
Exclusivity Classes and Partitions of Loss Functions
StanisÅaw M. S. Halkiewicz
Loss functions define estimator optimality, yet current decision-theoretic tools say little about when different losses demand incompatible optimal procedures. This paper introduce…
Variance Estimation for Saturated Fixed-Effect Specifications
StanisÅaw M. S. Halkiewicz
We characterize the asymptotic behavior of conventional variance estimators in linear regression with high-dimensional fixed effects under a drift in which both the proportional fi…
Split-Twin Extensions Preserving Seymour Vertices
StanisÅaw M. S. Halkiewicz
The Second Neighborhood Conjecture of Seymour asserts that every oriented graph contains a vertex~ satisfying $|\Npp(v)|\ge|\Np(v)|$. We introduce \emph{Pisa graphs} -- strongly…
Testing the Significance of the Difference-in-Differences Coefficient via Doubly Randomised Inference
StanisÅaw Marek Sergiusz Halkiewicz, Andrzej KaÅuża
This article develops a significance test for the Difference-in-Differences (DiD) estimator based on dual-margin randomization, in which both the treatment and time indicators are…