The optimized portfolio that is calculated by a covariance matrix has large sensitivities to small eigen values of the covariance matrix. Estimation of sampling errors for small eigen values is quite important for fund managers who construct their portfolios from estimated covariance matrixes....

In this paper, I present a simple characterization of the sample selection bias problem that is also applicable to the conceptually distinct econometric problems that arise from truncated samples and from models with limited dependent variables. The problem of sample selection bias is fit within...

This chapter summarizes recent literature on asymptotic inference about forecasts. Both analytical and simulation based methods are discussed. The emphasis is on techniques applicable when the number of competing models is small. Techniques applicable when a large number of models is compared to...

The quality of historical US census data is critical to the performance of linking algorithms. We use genealogical profiles to correct measurement errors in census names and ages. Our findings suggest that one in every two records has an error in name or age, and human capital is correlated with...

In recent years, an impressive body or research on predictive accuracy testing and model comparison has been published in the econometrics discipline. Key contributions to this literature include the paper by Diebold and Mariano (DM: 1995) that sets the groundwork for much of the subsequent work...

This paper studies the identification of coefficients in generalized linear predictors where the outcome variable suffers from non-classical measurement errors. Combining a mixture model of data errors with the bounding procedure proposed by Stoye (2007), I derive bounds on the coefficient...