Diagnostic Analysis and Computational Strategies for Estimating Discrete Time Duration Models - A Monte Carlo Study
(with Barry Smith)
Journal of Econometrics , Volume 187, Issue 1, July 2015, pp. 275-292
Abstract: This paper uses Monte Carlo analysis to study important and contentious issues in estimating single-spell discrete time duration models. We find simulated annealing dominates gradient methods for recovering true models. We recommend a partially flexible step function for duration dependence combined with likelihood ratio tests for determining support points of unobserved heterogeneity. We find that ignoring time-changing features of explanatory variables introduces substantial biases in model coefficient and average partial effect estimates. These biases do not diminish as sample size increases.
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Propensity Score Matching, a Distance-Based Measure of Migration, and the Wage Growth of Young Men
(with John C. Ham and Patricia B. Reagan)
Journal of Econometrics , 161(2), 2011, pp.208-227
Abstract: Our paper estimates the effect of US internal migration on wage growth for young men between their first and second job. Our analysis of migration extends previous research by: (i) exploiting the distance-based measures of migration in the National Longitudinal Surveys of Youth 1979 (NLSY79); (ii) allowing the effect of migration to differ by schooling level and (iii) using propensity score matching to estimate the average treatment effect on the treated (ATET) for movers and (iv) using local average treatment effect (LATE) estimators with covariates to estimate the average treatment effect (ATE) and ATET for compliers. We believe the Conditional Independence Assumption (CIA) is reasonable for our matching estimators since the NLSY79 provides a relatively rich array of variables on which to match. Our matching methods are based on local linear, local cubic, and local linear ridge regressions. Local linear and local ridge regression matching produce relatively similar point estimates and standard errors, while local cubic regression matching badly over-fits the data and provides very noisy estimates. We use the bootstrap to calculate standard errors. Since the validity of the bootstrap has not been investigated for the matching estimators we use, and has been shown to be invalid for nearest neighbor matching estimators, we conduct a Monte Carlo study on the appropriateness of using the bootstrap to calculate standard errors for local linear regression matching. The data generating processes in our Monte Carlo study are relatively rich and calibrated to match our empirical models or to test the sensitivity of our results to the choice of parameter values. The estimated standard errors from the bootstrap are very close to those from the Monte Carlo experiments, which lends support to our using the bootstrap to calculate standard errors in our setting. From the matching estimators we find a significant positive effect of migration on the wage growth of college graduates, and a marginally significant negative effect for high school dropouts. We do not find any significant effects for other educational groups or for the overall sample. Our results are generally robust to changes in the model specification and changes in our distance-based measure of migration. We find that better data matters; if we use a measure of migration based on moving across county lines, we overstate the number of moves, while if we use a measure based on moving across state lines, we understate the number of moves. Further, using either the county or state measures leads to much less precise estimates.
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Public Policy and the Dynamics of Children’s Health Insurance, 1986-1999
(with John Ham and Lara Shore-Sheppard)
American Economic Review Papers and Proceedings , 99(2), 2009, pp.522-526
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Propensity Score Matching and Abnormal Performance After Seasoned Equity Offerings
(with Xinlei Zhao)
Journal of Empirical Finance , 13(3), 2006, pp.351-370
Abstract: The long-run underperformance of stocks after seasoned equity offerings (SEOs) is a major challenge to the efficient market hypothesis. We reexamine the SEO underperformance anomaly using the propensity score matching method on a sample of around 2000 offerings between 1986 and 1998. While underperformance characterizes equal-weight and buy-and-hold returns if traditional matching methods are used, the underperformance is economically and statistically insignificant when we match issuers to non-issuers by propensity scores. Our results suggest that SEO underperformance manifests statistical inadequacies of traditional matching methods rather than an anomaly challenging the efficient market hypothesis.
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