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  • Victor Chernozhukov, Christian Hansen, Josh Angrist, Moshe Buchinsky, Jin Hahn, James Heckman +6 others
  • 2001
1 Headnote.The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g. education, prices) are often endogenous, making conventional quantile regression(More)
The most common approach to estimating conditional quantile curves is to fit a curve, typically linear, pointwise for each quantile. Linear functional forms, coupled with pointwise fitting, are used for a number of reasons including parsimony of the resulting approximations and good computational properties. The resulting fits, however, may not respect a(More)
Estimation of reference growth curves for children's height and weight has traditionally relied on normal theory to construct families of quantile curves based on samples from the reference population. Age-specific parametric transformation has been used to significantly broaden the applicability of these normal theory methods. Non-parametric quantile(More)
The main challenges in estimating strategic network formation models are the presence of multiple equilibria, and the fact that the number of possible network configurations increases exponentially with the number of players. I propose a dynamic model of strategic network formation with heterogeneous players, which converges to a unique stationary(More)
  • Manuel Arellano Cemfi, Madrid Stéphane, Bonhomme Cemfi, Madrid, Andrew Chesher, Bryan Graham +4 others
  • 2013
We introduce a class of linear quantile regression estimators for panel data. Our framework contains dynamic autoregressive models, models with general predetermined regressors, and models with multiple individual effects as special cases. We follow a correlated random-effects approach, and rely on additional layers of quantile regressions as a flexible(More)
Empirical Bayes methods for Gaussian compound decision problems involving longitudinal data are considered. The new convex optimization formulation of the nonparametric (Kiefer-Wolfowitz) maximum likelihood estimator for mixture models is employed to construct nonparametric Bayes rules for compound decisions. The methods are first illustrated with some(More)
  • A Belloni, V Chernozhukov, I Fern´andez-Val, And C Hansen, Andres Santos, Alberto Abadie +11 others
  • 2015
In this paper, we consider estimation of general modern moment-condition problems in econometrics in a data-rich environment where there may be many more control variables available than there are observations. The framework we consider allows for a continuum of target parameters and for Lasso-type or Post-Lasso type methods to be used as estimators of a(More)
This paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables. We provide conditions under which the slope parameter estimator is asymptotically Gaussian. Monte Carlo studies are carried out to investigate the finite sample performance of the proposed method in(More)
  • Ricardo Perez-Truglia, Guillermo Cruces, Alberto Alesina, Robert Barro, Raj Chetty, Monica Singhal +25 others
  • 2015
We study the role of partisan interactions for political participation and geographic polarization. We sent letters to 92,000 contributors from all U.S. states during the 2012 presidential election campaign. We used administrative data to measure the effect of the information contained in those letters on the recipients' subsequent contributions. We found(More)