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The Hausman test is used in applied economic work as a test of misspecification. It is most commonly thought of as a test of whether one or more explanatory variables in a regression model are endogenous. The usual Hausman contrast test requires one estimator to be efficient under the null hypothesis. If data are heteroskedastic, the least squares estimator is no longer efficient. The first option is to estimate the covariance matrix of the difference of the contrasted estimators, as suggested by Hahn, Ham, and Moon (2011). Other options for carrying out a Hausman-like test in this case include estimating an artificial regression and using robust standard errors. Alternatively, we might seek additional power by estimating the artificial regression using feasible generalized least squares. Finally, we might stack moment conditions leading to the two estimators and estimate the resulting system by GMM. We examine these options in a Monte Carlo experiment. We conclude that the test based on the procedure by Hahn, Ham, and Moon has good properties. The generalized least squares-based tests have higher size-corrected power when heteroskedasticity is detected in the DWH regression, and the heteroskedasticity is associated with a strong external IV. We do not consider the properties of the implied pretest estimator.

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

The Heckman two-step estimator (Heckit) for the selectivity model is widely applied in Economics and other social sciences. In this model a non-zero outcome variable is observed only if a latent variable is positive. The asymptotic covariance matrix for a two-step estimation procedure must account for the estimation error introduced in the first stage. We examine the finite sample size of tests based on alternative covariance matrix estimators. We do so by using Monte Carlo experiments to evaluate bootstrap generated critical values and critical values based on asymptotic theory.

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

The purpose of this chapter is to assess the importance of individual social capital characteristics in determining wages, both directly through their valuation by employers and indirectly through their impact on individual occupational choice. We find that a person’s level of sociability and care for others works through both channels to explain wage differences between social and nonsocial occupations. Additionally, expected wages in each occupation type are found to be at least as important as a person’s level of social capital in choosing a social occupation. We make use of restricted 2000 Decennial Census and 2000 Social Capital Community Benchmark Survey.

This chapter investigates the effect of financial performance and corporate governance on market performance, using evidence from the companies listed on the IDX30 Index of the Indonesia Stock Exchange (IDX) from 2015 to 2018. The authors use six main independent variables and one dependent variable, controlled by using control variables in the regression analysis. Ordinary least square (OLS) regression methods are used to model the relationship between the dependent variable and the independent variables. The results show that the current ratio (CR) and Board Size (BS) have a significant negative effect on stock return (SR). In contrast, the quick ratio (QR) and debt to equity ratio (DER) have a significant positive impact on SR. Both the debt to asset ratio (DAR) and Independent Board of Commissioners (BOC) have an insignificant effect on SR. This evidence suggests that the CR, QR, DER, and BS are essential factors affecting SR.

Little is known about perceptions of medical expenditure risks despite their presumed relevance to the demand for health insurance. This is the first study to examine households’ beliefs about their future spending on health care. The study made a unique elicitation of subjective probabilities of medical expenditures from rural Ethiopians participating in a panel survey and offered the opportunity to enrol in a health insurance programme. The vast majority of respondents give logically consistent responses to the subjective probability questions. The data indicate that the cross-sectional variance of realized expenditures, which is often used to proxy risk exposure, greatly overestimate the risk faced by any single household. Consistent with the serial correlation observed in realized expenditures, expectations are positively correlated with past expenses. They are revised upward in response to an increase in realized expenditure and, to some extent, they predict expenditure incurred in the year ahead. Despite containing information on future medical expenditures, there is no evidence that expectations influence the decision to take out health insurance, although plans to insure are positively related to the perceived volatility of expenses.

These results suggest that adverse selection may not threaten the viability of voluntary health insurance. A caveat is that measurement error in the reported probabilities may weaken the test for adverse selection. Notwithstanding this limitation, measurement of household-specific distributions of future medical expenses is feasible and avoids relying on the cross-sectional variance, which provides an upwardly biased estimate of medical expenditure risk.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.