James L. Powell
Department of Economics
508-1 Evans Hall #3880
Berkeley, CA 94720-3880
Institutional Affiliation: University of California at Berkeley
NBER Working Papers and Publications
|March 2015||Quantile Regression with Panel Data|
with Bryan S. Graham, Jinyong Hahn, Alexandre Poirier: 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...
|November 2008||Identification and Estimation of 'Irregular' Correlated Random Coefficient Models|
with Bryan S. Graham: w14469
In this paper we study identification and estimation of a correlated random coefficients (CRC) panel data model. The outcome of interest varies linearly with a vector of endogenous regressors. The coefficients on these regressors are heterogenous across units and may covary with them. We consider the average partial effect (APE) of a small change in the regressor vector on the outcome (cf., Chamberlain, 1984; Wooldridge, 2005a). Chamberlain (1992) calculates the semiparametric efficiency bound for the APE in our model and proposes a √N consistent estimator. Nonsingularity of the APE's information bound, and hence the appropriateness of Chamberlain's (1992) estimator, requires (i) the time dimension of the panel (T) to stric...
Published: Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models Bryan S. Graham1, James L. Powell2,† Article first published online: 25 SEP 2012 DOI: 10.3982/ECTA8220 © 2012 The Econometric Society Issue Econometrica Econometrica Volume 80, Issue 5, pages 2105–2152, September 2012
|1986||The Cyclical Behavior of Industrial Labor Markets: A Comparison of the Prewar and Postwar Eras|
with Ben S. Bernanke
in The American Business Cycle: Continuity and Change, Robert J. Gordon, editor
|June 1984||The Cyclical Behavior of Industrial Labor Markets: A Comparison of the Pre-War and Post-War Eras|
with Ben S. Bernanke: w1376
This paper studies the cyclical behavior of a number of industrial labor markets of the pre-war (1923-1939) and post-war (1954-1982) eras. In the spirit of Burns and Mitchell we do not test a specific structural model of the labor market but instead concentrate on describing the qualitative features of the (monthly, industry-level) data.The two principal questions we ask are: First, how is labor input (as measured by the number of workers, the hours of work, and the intensity of utilization) varied over the cycle ? Second, what is the cyclical behaviorof labor compensation (as measured by real wages, product wages, and real weekly earnings) ? We study these questions in both the frequency domain and the time domain. Many of our findings simply reinforce, or perhaps refine, existing percept...
Published: Bernanke, Ben S. and James L. Powell. "The Cyclical Behavior of Industrial Labor Markets: A Comparison of the Pre-War and Post-War Eras." The American Business Cycle: Continuity and Change, editedby Robert J. Gordon. Chicago: UCP, 1986, pp. 583-621 and 633-637.