Professor at the University of Chicago-Department of Economics
I did my PhD at CREST and Université Paris I, under the supervision of Jean-Marc Robin.
You can see my CV HERE.
Econometrics, Microeconometrics, Labour economics.
I teach Econometrics at the graduate level.
You can see some class notes at the bottom of this page.
Identification in a Binary Choice Panel Data Model with a Predetermined Covariate (with Bryan Graham and Kevin Dano)
Keywords: Feedback, Panel Data, Incidental Parameters, Partial Identification.
Summary: Identification in nonlinear panel data models where covariates are not strictly exogenous is poorly understood. We study a binary choice model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). We find that failure of point identification, yet the identified set can be informative even in very short panels.
Heterogeneity of Consumption Responses to Income Shocks in the Presence of Nonlinear Persistence (with Manuel Arellano, Richard Blundell, and Jack Light)
Keywords: Nonlinear income persistence, consumption dynamics, partial insurance, heterogeneity, panel data.
Summary: We study the nonlinear transmission of income shocks to consumption in the PSID, 2005-2017. We document that consumption responses are heterogeneous across households. Low-consumption types respond more strongly to income shocks at the beginning of the life cycle and when their assets are low, as standard life-cycle theory would predict. In contrast, high-consumption types respond less on average, and in a way that changes little with age or assets.
Income Risk Inequality: Evidence from Spanish Administrative Records (with Manuel Arellano, Micole de Vera, Laura Hospido, and Siqi Wei)
Keywords: Spain, income dynamics, administrative data, income risk, inequality.
Summary: We propose a method to measure individual income risk using administrative data. We document that income risk is highly unequal in Spain: more than half of the economy has close to perfect predictability of their income, while some face considerable uncertainty.
Supplementary appendix to the paper
Revision requested at Quantitative Economics, special issue on Global Income Dynamics.
Published and forthcoming papers
Minimizing Sensitivity to Model Misspecification (with Martin Weidner)
To appear in Quantitative Economics
Keywords: Model misspecification, robustness, sensitivity analysis.
Summary: We propose an approach to estimation and inference in the presence of model misspecification. In our first application, the form of the household utility function may be misspecified, while in our other two applications misspecification affects the functional form of the density of unobservables.
How Much Should We Trust Estimates of Firm Effects and Worker Sorting, (with Thibaut Lamadon, Magne Mogstad, Bradley Setzler, Kerstin Holzheu, and Elena Manresa)
To appear in the Journal of Labor Economics
Keywords: Matched employer employee data, worker and firm fixed-effects, sorting.
Summary: We revisit influential conclusions obtained using fixed-effects estimates of worker and firm heterogeneity in wage regression. Using data sources from multiple countries and several econometric methods, we find that conventional estimates suffer from large biases.
A package (Python, Stata) to implement the bias-correction methods in the paper is available here
Posterior Average Effects (with Martin Weidner)
To appear in the Journal of Business and Economic Statistics
Keywords: Model misspecification, robustness, sensitivity analysis, posterior estimators, latent variables.
Summary: We consider the estimation of expectations taken with respect to a latent distribution. We show that posterior estimators, which are computed conditional on the sample, enjoy robustness properties when the latent distribution is misspecified.
Appendix to the paper
Codes to replicate the results
Teams: Heterogeneity, Sorting and Complementarity
To appear in Proceedings of the 15th World Congress of the Econometric Society
Keywords: Networks, production, complementarity, sorting.
Summary: How much do individuals contribute to team output? I propose an econometric framework to quantify individual contributions when only the output of their teams is observed.
Slides of the presentation at the 2020 World Congress of the Econometric Society.
Codes to estimate additive and nonlinear models of team production
Recovering Latent Variables by Matching (with Manuel Arellano)
To appear in the Journal of the American Statistical Association
Keywords: Unobserved heterogeneity, nonparametric estimation, matching, factor models, optimal transport.
Summary: We propose a matching method based on optimal transport to nonparametrically estimate linear models with independent latent variables. We apply the method to document the cyclicality of permanent and transitory income and wage shocks in the US.
Codes to implement the matching estimator.
Discretizing Unobserved Heterogeneity (with Thibaut Lamadon and Elena Manresa)
To appear in Econometrica
Keywords: Unobserved heterogeneity, dimension reduction, panel data, structural estimation.
Summary: We develop two-step and iterative estimators based on a discretization of unobserved individual heterogeneity. We view discrete estimators as approximations, and study their properties in environments where population heterogeneity is unrestricted.
Codes. See also the GitHub page
A Distributional Framework for Matched Employer Employee Data (with Thibaut Lamadon and Elena Manresa)
Econometrica, 87(3), 699-738, May 2019.
Keywords: Matched employer employee data, sorting, job mobility, models of heterogeneity.
Summary: We develop methods to estimate the contributions of firms and workers to earnings dispersion that allow for complementarities and dynamics.
R package for the estimator, with examples
Econometric Analysis of Bipartite Networks
To appear in The Econometric Analysis of Network Data, edited by B. Graham and A. de Paula.
Keywords: Bipartite graph, network analysis, unobserved heterogeneity.
Summary: We review econometric techniques to analyze bipartite networks. We study fixed-effects, random-effects, and discrete heterogeneity approaches in linear and nonlinear models. We also discuss how to account for endogenous link formation and network dynamics.
Discussion of "On the Informativeness of Descriptive Statistics for Structural Estimates" by Isaiah Andrews, Matt Gentzkow and Jesse Shapiro
Comment, to appear in Econometrica.
Discussion of "Transparency in Structural Research" by Isaiah Andrews, Matt Gentzkow and Jesse Shapiro
Comment, to appear in the Journal of Business and Economic Statistics.
Nonlinear Persistence and Partial Insurance: Income and Consumption Dynamics in the PSID (with Manuel Arellano and Richard Blundell)
American Economic Review Papers and Proceedings, vol. 108, May 2018.
Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework (with Manuel Arellano and Richard Blundell)
Econometrica, 85(3), 693-734, May 2017.
Keywords: Earnings Dynamics, Consumption, Latent Variables.
Summary: We develop a flexible framework to study the nonlinear relationship between shocks to household earnings and consumption over the life cycle.
Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models (with Manuel Arellano)
Annual Review of Economics, 9, 471-496, August 2017.
Keywords: Dynamic Models, Structural Economic Models, Panel Data, Unobserved Heterogeneity.
Summary: A review of work on identification and estimation of nonlinear dynamic systems and their relationships with structural economic models with heterogeneous agents.
Quantile Selection Models (with Manuel Arellano)
Econometrica, 85(1), 1-28, January 2017.
Keywords: Quantile regression, sample selection, gender wage gap, copula.
Summary: We propose a method to correct quantile regression estimates for sample selection, and apply it to study wage and employment in the UK.
Code of the quantile selection estimator (in matlab), to be used on any data set.
A Stata command arhomme is now available, written by Martin Biewen and Pascal Erhardt. Type "ssc install arhomme" to install the package.
Survey on Sample Selection in Quantile Regression, with Manuel Arellano.
Handbook of Quantile Regression, R. Koenker, V. Chernozhukov, X. He, and L. Peng, eds. 2018
Nonlinear Panel Data Estimation via Quantile Regressions (with Manuel Arellano)
Econometrics Journal, 19(3), C61-C94, October 2016.
Keywords: Panel data, quantile regression, Expectation-Maximization.
Summary: We introduce a class of quantile regression estimators for short panels. Our correlated random-effects approach uses quantile regressions as flexible tools to model conditional distributions.
Keeping the Econ in Econometrics: (Micro-)Econometrics in the Journal of Political Economy (with Azeem Shaikh)
Journal of Political Economy (Special Issue on the 125th Anniversary of the Journal), Vol. 125, 6, 1846-1853, December 2017.
Summary: A review of several seminal econometric contributions published by the JPE.
The Cycle of Earnings Inequality: Evidence from Spanish Social Security Data (with Laura Hospido)
Economic Journal, 127, 1244-1278 August 2017.
Keywords: Earnings Inequality, Social Security data, Unemployment, Business cycle.
Summary: We use Spanish Social Security data to document the evolution of earnings inequality since the end of the 1980's. Male inequality is strongly countercyclical, and partly reflects the effects of the housing boom and bust on the construction sector.
A complement of the paper using tax data is:
Earnings Inequality in Spain: New Evidence Using Tax Data (with Laura Hospido).
Applied Economics, 45, 2013, 4212-4225.
School Characteristics and Teacher Turnover: Assessing the Role of Preferences and Opportunities (with Grégory Jolivet and Edwin Leuven)
Economic Journal, 126(594), 1342-1371, August 2016.
Keywords: labor turnover, compensating differentials, teacher labor markets, sample selection.
Summary: We propose a method to estimate workers' preferences for job amenities using data on job changes. We apply it to administrative data on Dutch primary school teachers.
Estimating Multivariate Latent-Structure Models (with Koen Jochmans and Jean-Marc Robin)
Annals of Statistics, 44(2), 540-563, April 2016.
Keywords: finite-mixture models, hidden Markov models, simultaneous matrix diagonalization.
Summary: We develop an approach to identify and estimate latent structure models, such as finite mixture models or hidden Markov models.
A companion paper is Nonparametric Estimation of Non-Exchangeable Latent Variable Models (with Koen Jochmans and Jean-Marc Robin)
To appear in the Journal of Econometrics
Nonparametric Estimation of Finite Mixtures From Repeated Measurements (with Koen Jochmans and Jean-Marc Robin)
Journal of Royal Statistical Society, Series B, 78(1), 211-229, January 2016.
Keywords: finite-mixture models, repeated-measurement data, re-weighting, two-step estimation.
Summary: We develop a practical two-step procedure to nonparametrically estimate finite mixture models from data on repeated measurements.
Grouped Patterns of Heterogeneity in Panel Data (with Elena Manresa)
Econometrica, 83(3), 1147-1184, May 2015.
Keywords: Discrete heterogeneity, panel data, fixed effects, democracy.
Summary: We propose an alternative to fixed-effects estimation in linear panel data regression that allows for group-level time-varying unobservables. We use this approach to document the evolution of income and democracy in the last part of the XXth century.
Stata code to compute the grouped fixed-effects estimator on your data
Econometrica, 80(4), 1337-1385, July 2012.
Keywords: Panel data, incidental parameters, inverse problems.
Summary: We propose a general method (a ``nonlinear within transformation'') to difference out the individual fixed effects in likelihood-based panel data models.
Identifying Distributional Characteristics in Random Coefficients Panel Data Models (with Manuel Arellano)
Review of Economic Studies, 79(3), 987-1020, July 2012.
Keywords: Panel data, random coefficients, multiple effects, nonparametric identification.
Summary: We provide identification results and construct estimators for variances, and more generally densities, of individual fixed effects in linear panel data models.
Penalized Least Squares Methods for Latent Variables Models
Discussion of papers prepared for the World Congress of the Econometric Society (Shanghai, 2010), 338-352, September 2011.
Keywords: Latent variables models, lasso, penalization.
Summary: A discussion of papers by Susanne Schennach and Victor Chernozhukov. We apply a penalized least squares (``lasso'') density estimator to a simple measurement error model.
Nonlinear Panel Data Analysis (with Manuel Arellano)
Annual Review of Economics, 3, 395-424, 2012.
Keywords: Panel data, incidental parameters, partial identification.
Summary: A survey of recent advances in panel data, including a discussion of the computation of random-effects estimators, and partial identification.
Recovering Distributions in Difference-in-Differences: A Comparison of Selective and Comprehensive Schooling (with Ulrich Sauder)
Review of Economics and Statistics, 93(2), 479–494, May 2011.
Keywords: Selective education, difference-in-differences, treatment effects, quantiles.
Summary: We introduce a method to estimate distributions of potential outcomes in difference-in-differences models. We apply it to study the effect of selective secondary education in the UK.
Generalized Nonparametric Deconvolution with an Application to Earnings Dynamics (with Jean-Marc Robin)
Review of Economic Studies, 77(2), 491-533, April 2010.
Keywords: Factor models, nonparametric deconvolution, earnings dynamics.
Summary: We propose a nonparametric estimator of factor distributions in linear factor independent factor models. We use it to estimate the distribution of earnings shocks in a simple permanent/transitory model, on PSID data.
The Pervasive Absence of Compensating Differentials (with Grégory Jolivet)
Journal of Applied Econometrics, 24(5), 763-795, June 2009.
Keywords: Compensating wage differentials, job mobility, amenities.
Summary: We build and estimate a structural job search model of wages, non-wage amenities, and job mobility on European data. We find strong preferences for some amenities, which are not reflected in wage/amenity correlations.
Consistent Noisy Independent Component Analysis (with Jean-Marc Robin)
Journal of Econometrics, 149(1), 12-25, April 2009.
Keywords: Factor analysis, Independent Component Analysis, higher-order moments.
Summary: We propose a method to estimate linear independent factor models using higher-order moments. We apply it to schooling data on test scores, and to the Fama-French data on stock returns.
Gauss codes for the ``quasi-JADE'' estimator
Robust Priors in Nonlinear Panel Data Models (with Manuel Arellano)
Econometrica, 77(2), 489-536, March 2009.
Keywords: Panel data, integrated likelihood, bias reduction
Summary: We show under which condition panel data integrated likelihood estimators reduce first-order bias on common parameters and average marginal effects. Our framework covers fixed-effects, random-effects, and Bayesian approaches as special cases.
Robust Priors for Average Marginal Effects: Comment
Assessing the Equalizing Force of Mobility Using Short Panels: France, 1990-2000 (with Jean-Marc Robin)
Review of Economic Studies, 76(1), 63-92, January 2009.
Keywords: Inequality, mobility, earnings dynamics, copula.
Summary: We build a model of earnings dynamics that combines a flexible modelling of the marginal distributions (inequality) with a tight parameterization of the dynamics (mobility). We use it to compute inequality in Present Values. We estimate the model on the French Labor Force Survey.
Standard Errors Estimation in Mixtures of Partial Likelihood Models
Keywords: EM algorithm, standard errors.
Summary: A simple method to estimate asymptotic standard errors of sequential EM estimators, using the generalized information identity.
Statistical Methods in Econometrics
3. Maximum Likelihood and asymptotic tests
5. Regression with Dependent Data
7. Generalized Method of Moments
8. Limited Dependent Variables
A gentle introduction to quantile regression