Quantile regression(QR) fits a linear model for conditional quantiles, just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean square error linear approximation to the conditional expectation function even...

In this paper, we propose a new approach to the estimation of social networks and we apply it to the estimation of productivity spillovers in the U.S. Congress. Social networks such as the social connections among lawmakers are not generally directly observed, they can be recovered only using...

This paper examines the effect of antidepressant use on the likelihood of being employed among HIV-positive women receiving highly active antiretroviral therapy (HAART) in the United States from 1994 to 2004. We use instrumental variables to predict antidepressant use independently of outcomes;...

Using U.S. Bureau of Economic Analysis data for individual foreign acquisitions and new establishments in the U.S from 1988 to 1998, and aggregate data for 1980 to 1998, we find that acquisitions and establishments of new firms tend to occur in periods of high U.S. growth and take place mainly...

This paper examines the recent upsurge in foreign acquisitions of U.S. firms, specifically focusing on acquisitions made by firms located in emerging markets. Neoclassical theory predicts that, on net, capital should flow from countries that are capital-abundant to countries that are...

We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables. We treat models in which Y is censored from above or below or potentially from both. The basic idea...

We use a dynamic panel Tobit model with heteroskedasticity to generate point, set, and density forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous...

We consider the sensitivity of the Tobit estimator to heteroscedasticity. Our single independent variable is a dummy variable whose coefficient is a difference between group means, and the error variance differs between groups. Heteroscedasticity biases the Tobit estimate of the two means in...

Applied economists have long struggled with the question of how to accommodate binary endogenous regressors in models with binary and non-negative outcomes. I argue here that much of the difficulty with limited-dependent variables comes from a focus on structural parameters, such as index...

Discussions of financial risk often fail to distinguish between risks that are consciously borne and those that are not. To understand the breeding conditions for financial crises the prime focus of concern should not be simply on large risk-taking per se, but on the unintended, or unanticipated...