While stock market expectations are among the most important primitives of portfolio choice models, their measurement has proved challenging for some respondents. We argue that the magnitude of measurement error in subjective expectations can be used as an indicator of the degree to which...

While stock market expectations are among the most important primitives of portfolio choice models, their measurement has proved challenging for some respondents. We argue that the magnitude of measurement error in subjective expectations can be used as an indicator of the degree to which...

This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC...

In this paper the extensive empirical literature on the gender wage gap is reviewed with particular attention given to the identification of the key parameters in the specified human capital wage regression models. This aspect has been of great importance in the literature chiefly for two...

This survey is devoted to the modelling and the estimation of reduced-form transition models, which have been extensively used and estimated in labor microeconometrics. The first section contains a general presentation of the statistical modelling of such processes using continuous-time...

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of...

There is a large literature estimating Arrow-Pratt coefficients of absolute and relative risk aversion. A striking feature of this literature is the very wide variation in the reported estimates of the coefficients. While there are often legitimate reasons for these differences in the estimates,...

Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of...

In this paper, we propose a method to evaluate the effect of a counterfactual change in the unconditional distribution of a single covariate on the unconditional distribution of an outcome variable of interest. Both fixed and infinitesimal changes are considered. We show that such effects are...

In this paper, we propose a new approach to estimating sample selection models that combines Generalized Tukey Lambda (GTL) distributions with copulas. The GTL distribution is a versatile univariate distribution that permits a wide range of skewness and thick- or thin-tailed behavior in the data...