Linear Estimation of Structural and Causal Effects for Nonseparable Panel Data
arXiv:2607.28291
The paper proposes linear sieve estimators with bias‑corrected ridge regressions to identify structural and causal effects in nonseparable panel data models that feature time‑varying unobserved heterogeneity, and demonstrates applications to consumer welfare and income elasticity analysis.
Abstract
This paper develops linear estimators for structural and causal parameters of nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of a conditional average potential outcome by a linear sieve specification with individual-specific parameters. Effects of interest are estimated by a bias corrected average of individual ridge regressions. We demonstrate how this approach can be applied to estimate causal effects, counterfactual consumer welfare, and averages of individual taxable income elasticities. We show that the proposed estimator has an empirical Bayes interpretation and possesses a number of other useful properties. We formulate Large- asymptotics that can accommodate discrete regressors and which bypass partial identification in this case. We employ the methods to estimate average equivalent variation and deadweight loss for potential price increases using data on grocery purchases.