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Inference on the Quantile Regression Process

Econometrica 2002 70(4), 1583-1612 open access
Abstract. Tests based on the quantile regression process can be formulated like the classical Kolmogorov-Smirnov and Cramer-von-Mises tests of goodness-of-t employing the theory of Bessel processes as in Kiefer (1959). However, it is fre-quently desirable to formulate hypotheses involving unknown nuisance parameters, thereby jeopardizing the distribution free character of these tests. We characterize this situation as \ he Durbin problem " since it was posed in Durbin (1973), for parametric empirical processes. In this paper we consider an approach to the Durbin problem involving a mar-tingale transformation of the parametric empirical process suggested by Khmaladze (1981) and show that it can be adapted to a wide variety of inference problems involving the quantile regression process. In particular, we suggest new tests of the location shift and location-scale shift models that underlie much of classical econometric inference. The methods are illustrated with a reanalysis of data on unemployment durations from the Pennsylvania Reemployment Bonus Experiments. The Pennsylvania ex-periments, conducted in 1988-89, were designed to test the ecacy of cash bonuses paid for early reemployment in shortening the duration of insured unemployment spells. 1.

The Reluctant Analyst

Journal of Accounting Research 2016 54(4), 987-1040
ABSTRACT We estimate the dynamics of recommendations by financial analysts, uncovering the determinants of inertia in their recommendations. We provide overwhelming evidence that analysts revise recommendations reluctantly, introducing frictions to avoid frequent revisions. More generally, we characterize the sources underlying the infrequent revisions that analysts make. Publicly available data matter far less for explaining recommendation dynamics than do the recommendation frictions and the long‐lived information that analysts acquire but the econometrician does not observe. Estimates suggest that analysts structure recommendations strategically to generate a profitable order flow from retail traders. We provide extensive evidence that our model describes how investors believe analysts make recommendations, and that investors value private information revealed by analysts' recommendations.