This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a "grouped fixed-effects" estimator that minimizes a least-squares criterion with respect to all possible groupings of the cross-sectional units. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries.JEL codes: C23.

Identifying distributional characteristics in random coefficients panel data models cemmap working paper, No. CWP22/09

We propose a method to correct for sample selection in quantile regression models. Selection is modeled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method‐of‐moments criterion. Given these parameter estimates, the percentile levels of the outcome are readjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978–2000. We also extend the method to account for the presence of equilibrium effects when performing counterfactual exercises.

SUMMARYWe study the relation between individual preferences for job amenities (e.g., type of work, job security) and compensating wage differentials in cross-section. To this end, we estimate a partial equilibrium job search model on panel data from eight European countries. There are five non-wage job characteristics and two sources of job-to-job mobility: on-the-job search and reallocation shocks. We also allow for two types of unobserved heterogeneity. We find strong preferences for amenities, especially job security, yet, these preferences do not translate into significant wage differentials in cross-section. Counterfactual experiments show that one would need extremely low levels of search frictions for compensating differentials to arise. Lastly, a similar exercise on the distribution of job change outcomes reveals the role of constrained job-to-job mobility in the absence of compensating wage differentials.

In this paper, we construct a non-parametric estimator of the distributions of latent factors in linear independent multi-factor models under the assumption that factor loadings are known. Our approach allows estimation of the distributions of up to "L"("L"+ 1)/2 factors given "L" measurements. The estimator uses empirical characteristic functions, like many available deconvolution estimators. We show that it is consistent, and derive asymptotic convergence rates. Monte Carlo simulations show good finite-sample performance, less so if distributions are highly skewed or leptokurtic. We finally apply the generalized deconvolution procedure to decompose individual log earnings from the panel study of income dynamics (PSID) into permanent and transitory components. Copyright © 2009 The Review of Economic Studies Limited.