In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni (1999, 2004). Results based on both simulated and real data suggest that different adjustment...

We investigate whether economic conditions at labor market entry predict long-term differences in law graduate earnings. We find that unemployment levels at graduation continue to predict law earnings premiums within 4 years after graduation for earners at the high end and middle of the...

We estimate the increase in earnings from a law degree relative to a bachelor's degree for graduates who majored in different fields in college. Students with humanities and social sciences majors comprise approximately 47 percent of law degree holders compared to 23 percent of terminal...

We estimate the increase in earnings from a law degree relative to a bachelor’s degree for graduates of different race/ethnic groups. Law earnings premiums are higher for whites than for minorities (excluding individuals raised outside the U.S.). The median annual law earnings premium is...

Estimation of standard errors of Engel elasticities within the framework of a linear structural model formulated on two-wave panel data is considered. The complete demand system is characterized by measurement errors in total expenditure and by latent preference variation. The estimation of the...

Estimation of standard errors of Engel elasticities within the framework of a linear structural model formulated on two-wave panel data is considered. The complete demand system is characterized by measurement errors in total expenditure and by latent preference variation. The estimation of the...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

I propose a nonparametric iid bootstrap that achieves asymptotic refinements for t tests and confidence intervals based on GMM estimators even when the model is misspecified. In addition, my bootstrap does not require recentering the moment function, which has been considered as critical for...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

A graphical processing unit (GPU) is a hardware device normally used to manipulate computer memory for the display of images. GPU computing is the practice of using a GPU device for scientific or general purpose computations that are not necessarily related to the display of images. Many...