Instrument Choice and the Returns to Education: New Evidence from Vietnam

This paper focuses on instrument choice while consistently estimating the returns to education in Vietnam. Using data culled from the 2 rounds of the Vietnam Living Standards Survey (VLSS), we explore different sets of exogenous instruments that rely on demand and supply side sources of variation in schooling as well as the matrix of instruments proposed by Hausman and Taylor (1981). Instrument validity tests suggest that many variables do not satisfy the necessary conditions allowing them to be used as instruments. As in several studies, we find that IV estimates of the returns to education are substantially higher than the corresponding OLS estimate. We show how the Hausman-Taylor matrix of instruments, when combined with other instruments, may be a useful way of consistently estimating an average return to education rather than a local average treatment effect (Angrist, 1994).vietnam, returns to education, Hahn-Hausman test, Hausman-Taylor estimator, instrument choice, instrumental variables procedures

Instrument Choice and the Returns to Education: New Evidence from Vietnam

This paper focuses on instrument choice while consistently estimating the returns to education in Vietnam. Using data culled from the 2 rounds of the Vietnam Living Standards Survey (VLSS), we explore different sets of exogenous instruments that rely on demand and supply side sources of variation in schooling as well as the matrix of instruments proposed by Hausman and Taylor (1981). Instrument validity tests suggest that many variables do not satisfy the necessary conditions allowing them to be used as instruments. As in several studies, we find that IV estimates of the returns to education are substantially higher than the corresponding OLS estimate. We show how the Hausman-Taylor matrix of instruments, when combined with other instruments, may be a useful way of consistently estimating an average return to education rather than a local average treatment effect (Angrist, 1994).ate of return, instrumental variables procedures, Instrument choice, Hausman-Taylor estimator, Hahn-Hausman test, Vietnam

Tests of Bias in Log-Periodogram Regression

This paper proposes simple Hausman-type tests to check for bias in the log-periodogram regression of a time series believed to be long memory. The statistics are asymptotically standard normal on the null hypothesis that no bias is present, and the tests are consistent.Long memory, log periodogram regression, Hausman test.

A robust bootstrap approach to the Hausman test in stationary panel data models

In panel data econometrics the Hausman test is of central importance to select an e?cient estimator of the models' slope parameters. When testing the null hypothesis of no correlation between unobserved heterogeneity and observable explanatory variables by means of the Hausman test model disturbances are typically assumed to be independent and identically distributed over the time and the cross section dimension. The test statistic lacks pivotalness in case the iid assumption is violated. GLS based variants of the test statistic are suitable to overcome the impact of nuisance parameters on the asymptotic distribution of the Hausman statistic. Such test statistics, however, also build upon strong homogeneity restrictions that might not be met by empirical data. We propose a bootstrap approach to specification testing in panel data models which is robust under cross sectional or time heteroskedasticity and inhomogeneous patterns of serial correlation. A Monte Carlo study shows that in small samples the bootstrap approach outperforms inference based on critical values that are taken from a X?-distribution. --Hausman test,random effects model,wild bootstrap,heteroskedasticity

Testing the exogeneity assumption in panel data models with "non classical" disturbances

This paper is concerned with the use of the Durbin-Wu-Hausman test for correlated effects with panel data. The assumptions underlying the construction of the statistic are too strong in many empirical cases. The consequences of deviations from the basic assumptions are investigated. The size distortion is assessed. In the case of measurement error, the Hausman test is found to be a test of the difference in asymptotic biases of between and within group estimators. However, its `size' is sensitive to the relative magnitude of the intra-group and inter-group variations of the covariates, and can be so large as to preclude the use of the statistic in this case. We show to what extent some assumptions can be relaxed in a panel data context and we discuss an alternative robust formulation of the test. Power considerations are presented. Keywords; models with panel data, hausman test, minimum variance estimators, quadratic forms in normal variables, monte carlo simulations

Robust exogeneity tests in the presence of outliers

Exogeneity testing is studied in the presence of outliers in response variables. Robust tests based on least absolute deviations (LAD) and M estimators are proposed and illustrated with an application to Mroz (1987) data. Our simulation results show that the proposed robust tests outperform the traditional Hausman test for exogeneity in terms of empirical power in the presence of outliers in response variables. Nevertheless, unlike the conventional Hausman test, which is undersized, the empirical size of the LAD-based exogeneity test exceeds its nominal size.Hausman exogeneity test Robust tests LAD estimator M estimator.

Robustness or Efficiency, A Test to Solve the Dilemma

When dealing with the presence of outliers in a dataset, the problem of choosing between the classical ordinary least squares and robust regression methods is sometimes addressed inadequately. In this article, we propose using a Hausman-type test to determine whether a robust S- estimator is more appropriate than an ordinary least squares one in a multiple linear regression framework, on the basis of the trade-off betewen robustness and efficiency. An economic example is provided to illustrate the usefulness of the test.Efficiency, Hausman Test, Linear Regression, Robustness, S- estimator

Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or not IV?

We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term.A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman- Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for timefixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h.s variables and residual term.This indicates that it is generally more efficient.The results are roughly robust for various combinations in the time and cross-section dimension of the data.Endogeneity, instrumental variables, two-step estimators, Monte Carlo simulations

Revisiting The Bell Curve Debate Regarding the Effects of Cognitive Ability on Wages

In The Bell Curve, Herrnstein and Murray (1994) claim, based on evidence from cross-sectional regressions, that differences in wages in the U.S. labor market are predominantly explained by general intelligence. Cawley, Heckman, and Vytlacil (1999), using evidence from random effects panel regressions, reject this claim, in part because returns to general intelligence vary by racial and gender subgroups in their results. In this article, we examine the regression methods used by both sides of the debate and conclude that neither is the appropriate method to analyze the NLSY data that both use. We introduce the Hausman-Taylor estimator to obtain consistent estimated coefficients on the time-invariant general intelligence-related variables and also extend the analysis up through 2002. While many additional socio-economic factors are important explanatory variables in determining the wage rate, the effect of general intelligence on wages is larger in the Hausman-Taylor specification for the 1979-1994 panel than in either the cross-sectional or random effects models, though it becomes statistically insignificant for the 1994-2002 panel. The Hausman-Taylor analysis also indicates no significantly different returns to intelligence by race or gender group.wages, cognitive ability, education