A Monte Carlo comparison of estimators for a bivariate probit model with selection

prototypical sample selection model consists of a two-equation system: one equation representing the selection mechanism and the second a continuous outcome variable that is only observed for the selected cases. A variant of this model where the outcome variable is binary leads to a bivariate probit model with sample selection. A Monte Carlo experiment is undertaken to examine the small sample properties of three alternative estimators of a bivariate probit model with selection. The three estimators are the censored probit estimator, single-equation probit applied to the selected sub-sample and single-equation probit applied to the full sample

A practical comparison of the bivariate probit and linear IV estimators

This paper presents asymptotic theory and Monte-Carlo simulations comparing maximum-likelihood bivariate probit and linear instrumental variables estimators of treatment effects in models with a binary endogenous treatment and binary outcome. The three main contributions of the paper are (a) clarifying the relationship between the Average Treatment Effect obtained in the bivariate probit model and the Local Average Treatment Effect estimated through linear IV; (b) comparing the mean-square error and the actual size and power of tests based on these estimators across a wide range of parameter values relative to the existing literature; and (c) assessing the performance of misspecification tests for bivariate probit models. The authors recommend two changes to common practices: bootstrapped confidence intervals for both estimators, and a score test to check goodness of fit for the bivariate probit model.Scientific Research&Science Parks,Science Education,Statistical&Mathematical Sciences,Econometrics,Educational Technology and Distance Education

Simplified implementation of the Heckman Estimator of the Dynamic Probit Model and a comparison with alternative estimators

This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model and other dynamic nonlinear panel data models using standard software. It then compares the estimators proposed by Heckman, Orme and Wooldridge, based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simulation experiments. The results indicate that none of the three estimators dominates the other two in all cases. In most cases all three estimators display satisfactory performance, except when the number of time periods is very small

Modeling Sample Selection for Durations with Time-Varying Covariates, With an Application to the Duration of Exchange Rate Regimes

We extend existing estimators for duration data that suffer from non-random sample selection to allow for time-varying covariates. Rather than a continuous-time duration model, we propose a discrete-time alternative that models the effects of sample selection at the time of selection across all subsequent years of the resulting spell. Properties of the estimator are compared to those of a naive discrete duration model through Monte Carlo analysis and indicate that our estimator outperforms the naive model when selection is non-trivial. We then apply this estimator to the question of the duration of monetary regimes and find evidence that ignoring selection into pegs leads to faulty inferences.exchange rates; de facto regimes; duration; selection models; monetary policy

Sample selection bias and the South African wage function

Conventional wage analyses suffers from a debilitating ailment: since there are no observable market wages for individuals who do not work, findings are limited to the sample of the population that are employed. Due to the problem of sample selection bias, using this subsample of working individuals to draw conclusions for the entire population will lead to inconsistent estimates. Remedial procedures have been developed to address this issue. Unfortunately, these models strongly rely on the assumed parametric distribution of the unobservable residuals as well as the existence of an exclusion restriction, delivering biased estimates if either of these assumptions is violated. This has given rise to a recent interest in semi-parametric estimation methods that do not make any distributional assumptions and are thus less sensitive to deviations from normality. This paper will investigate a few proposed solutions to the sample selection problem in an attempt to identify the best model of earnings for South African data.Semiparametric and nonparametric methods; Simulation methods; Truncated and censored models; Labour force and employment, Size, and structure

Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators

This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model and other dynamic nonlinear panel data models using standard software. It then compares the estimators proposed by Heckman, Orme and Wooldridge, based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simulation experiments. The results indicate that none of the three estimators dominates the other two in all cases. In most cases all three estimators display satisfactory performance, except when the number of time periods is very small.Dynamic discrete choice models ; initial conditions ; dynamic probit ; panel data ; dynamic nonlinear panel data models

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.treatment effects, propensity score, bias, unconfoundedness, selection on unobservables

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.Treatment Effects, Propensity Score, Bias, Unconfoundedness, Selection on Unobservables

The inter-related dynamics of unemployment and low-wage employment

This paper examines the extent of state dependence in unemployment and the role played in this by intervening low-wage employment. A range of dynamic random and fixed effects estimators are compared. Low-wage employment is found to have almost as large an adverse effect as unemployment on future prospects and the difference in their effects is found to be insignificant. Evidence is presented that low-wage jobs act as the main conduit for repeat unemployment and considerably increases its probability. Obtaining a higher-wage job reduces the increased risk of repeat unemployment to insignificance