University of California - Los Angeles
Los Angeles, CA 90095-1477.
Institutional Affiliation: University of California at Los Angeles
Information about this author at RePEc
NBER Working Papers and Publications
|March 2015||Quantile Regression with Panel Data|
with Bryan S. Graham, Alexandre Poirier, James L. Powell: w21034
We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the econometrician to (i) introduce dependence between the regressors and the random coefficients and (ii) weaken the assumption of comonotonicity across them (i.e., to enrich the structure of allowable dependence between different coefficients). We adopt a “fixed effects” approach, leaving any dependence between the regressors and the random coefficients unmodelled. We motivate different notions of quantile partial effects in our model and study their identification. For the case of discretely-valued c...
|August 2010||Estimation with Valid and Invalid Instruments|
with Jerry Hausman
in Contributions in Memory of Zvi Griliches, Jacques Mairesse and Manuel Trajtenberg, editors
|May 1999||Evaluating the Effect of an Antidiscrimination Law Using a Regression-Discontinuity Design|
with Petra Todd, Wilbert Van der Klaauw: w7131
The regression discontinuity (RD) data design is a quasi-experimental design with the defining characteristic that the probability of receiving treatment changes discontinuously as a function of one or more individual characteristics. This data design occasionally arises in economic and other applications but is only infrequently exploited in evaluating the effects of a treatment. We consider the problem of identification and estimation of treatment effects under a RD data design. We offer an interpretation of the IV or so-called Wald estimator as a regression discontinuity estimator. We propose nonparametric estimators of treatment effects and present their asymptotic distribution theory. Then we apply the estimation method to evaluate the effect of EEOC-coverage on minority employme...
|December 1998||Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange|
with Francis X. Diebold, Anthony S. Tay: w6845
We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast forecasts. Finally by recent advances in financial risk management, we provide a detailed application to multivariate high-frequency exchange rate density forecasts.
Published: (Published as "Multivariate Density Forecast Evaluation and Calibration in Financial Risk Management: High Frequency Returns on Foreign Exchange") Review of Economics and Statistics, Vol. 81 (1999): 661-673.