Grouped fixed effects regularization for binary choice models

Claudia Pigini; Alessandro Pionati; Francesco Valentini

Home/Archive/arXiv

arXiv · 2502.06446

Grouped fixed effects regularization for binary choice models

Claudia Pigini·Alessandro Pionati·Francesco Valentini

View original source ↗Read source document ↗Export metadata ↓

Abstract

We study the application of the grouped fixed effects approach to binary choice models for panel data in presence of severe complete separation. Through data loss, complete separation may lead to biased estimates of Average Partial Effects and imprecise inference. Moreover, forecasts are not available for units without variability in the response configuration. The grouped fixed effects approach discretizes unobserved heterogeneity via k-means clustering, thus reducing the number of fixed effects to estimate. This regularization reduces complete separation, since it relies on within-cluster rather than within-subject response transitions. Drawing from asymptotic theory for the APEs, we propose choosing a number of groups such that clustering delivers a good approximation of the latent trait while keeping the incidental parameters problem under control. The simulation results show that the proposed approach delivers unbiased estimates and reliable inference for the APEs. Two empirical applications illustrate the sensitivity of the results to the choice of the number of groups and how nontrivial forecasts for a much larger number of units can be obtained.

Explore related subjects

econ.EM

Keep this discovery

BibTeX RIS

Claudia Pigini, Alessandro Pionati, Francesco Valentini. 2025-02-10. Grouped fixed effects regularization for binary choice models. https://arxiv.org/abs/2502.06446

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM↗

arXiv2026

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM↗

arXiv2026

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM↗