Moshe Buchinsky

CHANGES IN THE U.S. WAGE STRUCTURE 1963-1987: APPLICATION OF QUANTILE REGRESSION

A recently developed quantile regression technique, which parsimoniously describes the entire conditional distribution, is applied to every March Current Population Survey since 1964. The study examines changes in the returns to schooling and experience at different points of the wage distribution and changes in within-group wage inequality. The results from the one-group and sixteen-group linear models show that the returns to schooling and experience differ across quantiles of the wage distribution but that their patterns of change are similar. Significant differences in wage inequality are also found across the various skill groups. Copyright 1994 by The Econometric Society.

The dynamics of changes in the female wage distribution in the USA: a quantile regression approach

This paper examines the female wage structure focusing on changes at different points in the wage distribution. Newly developed quantile regression methods are used in analysing data from the March Current Population Survey. The results show that while the most significant changes for the less skilled women took place at the bottom of the wage distribution, for the more skilled groups changes occurred at both ends of the distribution. Consequently, wage inequality decreased for the high-school graduates and increased for the younger college graduates. Furthermore, the more highly skilled women experienced the steepest gain in wages regardless of their position in the distribution.

A Three‐step Method for Choosing the Number of Bootstrap Repetitions

This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, confidence regions, hypothesis tests, p-values, and bias correction. For each of these problems, the paper provides a three-step method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the percentage deviation of the bootstrap standard error estimate, confidence interval length, tests critical value, tests p-value, or bias-corrected estimate based on B bootstrap simulations from the corresponding ideal bootstrap quantities for which B = infinity. The results apply to parametric, semiparametric, and nonparametric models with independent or dependent data.

An Alternative Estimator for the Censored Quantile Regression Model

This paper introduces an alternative estimator for the linear censored quantile regression model. The estimator also applies to cases where the censoring point is unknown. Since the objective function is globally convex and the estimator is a solution to a linear programming problem, a global minimizer is obtained in a finite number of simplex iterations. The estimator has a square root of n-convergence rate and is asymptotically normal. A Monte Carlo study performed shows that the suggested estimator has very desirable small sample properties.

An empirical analysis of the social security disability application, appeal, and award process

We provide an empirical analysis of the Social Security disability application, award, . and appeal process using the Health and Retirement Survey HRS . We show that the appeal option increases the award probability from 46% to 73%. However, this comes at the cost of significant delays: the duration between application and award is over three times longer for those who are awarded benefits after one or more stages of appeal. Our results reveal the importance of self-selection in application and appeal decisions. In particular, an individuals self-assessed disability status emerges as one of the most powerful predictors of application, appeal, and award decisions. q 1999 Elsevier Science B.V. All rights reserved.

Evaluation of a three-step method for choosing the number of bootstrap repetitions

Abstract This paper provides a variety of Monte Carlo simulations that evaluate the finite-sample performance of the three-step method for choosing the number of bootstrap repetitions, suggested by Andrews and Buchinsky (Econometrica 67 (2000) 23–51). The simulations cover bootstrap standard errors, confidence intervals, tests, and p-values. Three commonly used econometric applications are considered: linear regression, binary probit, and quantile regression. In brief, we find that the three-step method works very well in all of the contexts examined here. We also find that the number of bootstrap repetitions commonly used in econometric applications is much less than needed to achieve accurate bootstrap quantities.

ON THE NUMBER OF BOOTSTRAP REPETITIONS FOR BCa CONFIDENCE INTERVALS

This paper considers the problem of choosing the number bootstrap repetitions B to use with the BC_{a} bootstrap confidence intervals introduced by Efron (1987). Because the simulated random variables are ancillary, we seek a choice of B that yields a confidence interval that is close to the ideal bootstrap confidence interval for which B = infinity. We specifiy a three-step method of choosing B that ensures that the lower and upper lengths of the confidence interval deviate from those of the ideal bootstrap confidence interval by at most a small percentage with high probability.