Job market paper
Job Market Paper
Semiparametric Panel Data Models with Interactive Fixed Effects
Abstract
Methods for estimation and inference in semiparametric panel data models with both correlated random effects and interactive fixed effects are developed. The framework combines a Mundlak (1978) projection for the time-invariant component of unobserved heterogeneity with a local-linear filtering device, in the spirit of Huang (2013), for the time-varying component. Stacking the additive time effect and the interactive factor into a joint factor vector treats unobserved time heterogeneity uniformly. The estimator augments the partially linear regression with cross-sectional averages of the dependent variable and the regressors and recovers the parametric block by Robinson (1988) partialling, requiring no iteration, no principal components and no knowledge of the number of factors; coefficients on time-invariant regressors, which fixed effects absorb, are identified through the Mundlak projection. Asymptotic properties are characterized under joint large-N, large-T asymptotics, and Monte Carlo evidence documents finite-sample performance. The estimator is applied to the firm-level idea production function, with Bloom, Jones, Van Reenen and Webb (2020) as the parametric benchmark. Returns to research intensity are increasing, and the linearization through which the benchmark is implemented is rejected while the constant-elasticity form it approximates is not. The returns-to-scale parameter the benchmark sets by assumption is estimated here, at a value the data cannot distinguish from the assumed one but which points to ideas getting harder to find faster than the benchmark reports.
Publications
Journal of Risk and Uncertainty · 2026
Contests with Ambiguous Prizes
Abstract
This paper examines behavior in contests where the prize value is ambiguous. We develop a theoretical model of bidding in a Tullock contest with an ambiguous prize where contestants account for the ambiguity attitude of their rival. Ambiguity affects optimal behavior via two countervailing channels - a direct effect arising from contestants’ ambiguity about the value of the prize and an indirect effect corresponding to the effect of ambiguity on the opponent’s behavior. Using a controlled laboratory experiment, we elicit individual risk and ambiguity attitudes and compare predicted and observed behavior in contests with an ambiguous prize, a risky prize and certain prizes. A comparison between contests with ambiguous and risky prizes shows that participants invest significantly less under ambiguity. Additionally, we decompose the effect of changing from a certain prize to an ambiguous prize into two components - the first is the effect of introducing risk and the second is the effect of introducing ambiguity. Empirically, we find that both effects are significant, but work in opposite directions.
Advanced Studies in Theoretical and Applied Econometrics · 2025
Nonparametric Correlated Random-Effects Models
In Seven Decades of Econometrics and Beyond, vol. 57, 289–307.
Badi H. Baltagi and László Mátyás (eds.), Springer.
Abstract
This chapter develops methods for estimation and inference in nonparametric panel data models with correlated random-effects. Using the Mundlak specification to control for unobserved heterogeneity, this nonparametric estimation procedure can identify both the nonparametric function and a finite-dimensional parameter associated with (potentially) observed time-invariant regressors. We develop the necessary asymptotic theory for our proposed estimator. To assess the validity of our method in practice, we propose a consistent specification test for whether the model controls for the correlation between the unobserved individual effects and the regressors. Monte Carlo simulations support the asymptotic developments. We illustrate the practical utility of our approach via an empirical application.
Journal of Life Care Planning · 2019
Evaluation of Reasonableness of Fees for Life Care Plan Services in Vaccine Act Cases
Journal of Life Care Planning, 17(4), 31–37.
Under revision
Economics Letters
Nonlinear Panel Data Models with Robust Correlated Random Effects
Abstract
While linear panel data models with correlated random effects maintain consistency even under misspecification of the individual effect, this robustness property fails in nonlinear settings. We address this limitation by modeling the individual effect as an unknown smooth function of time-invariant covariates, allowing an unrestricted relationship between unobserved heterogeneity and the regressors. Estimation proceeds by profile least squares, yielding √N-consistent estimates of the structural parameters, which are identified from within-individual variation, while treating the nonparametric component as a nuisance function. Simulations confirm that the proposed estimator remains consistent when existing methods become biased and inconsistent. An application to the public capital productivity puzzle shows that flexible modeling of the production function and individual effects leads to positive and significant returns to public capital.
Conference presentations and invited seminars
References
Daniel J. Henderson, Chair
Robert and Mary Cobb Endowed Professor; Professor of Economics
Department of Economics, Finance & Legal Studies, University of Alabama
Rick and Elaine Horsley Faculty Fellow; Professor of Economics
Department of Economics, Finance & Legal Studies, University of Alabama
Department Chair; Professor of Economics
Department of Economics and Management, University of Cyprus