We introduce an estimation method that applies to a class of multivariate regression problems. The method can estimate parameters that are subject to multiple reduced-rank conditions and other parameter restrictions and the method allows for a general specifications of the covariance matrix. We...

This paper analyzes the finite-sample performance of the two-pass (TP) estimators of factor risk prices when betas have high cross-sectional correlations (Multicollinear) and when betas have small cross-sectional variations (Invariant). Our Monte Carlo simulations, calibrated using actual...

This paper examines the asymptotic and finite sample properties of the two-pass cross-sectional regressions estimators, when the factors and the asset returns are conditionally heteroskedastic and/or autocorrelated. Using a minimum distance approach, we derive heteroskedasticity- and/or...

We derive an identity for the determinant of a product involving non-squared matrices. The identity can be used to derive the maximum likelihood estimator in reduced-rank regressions with Gaussian innovations. Furthermore, the identity sheds light on the structure of the estimation problem that...

The semi parametric Gini regression is more robust than ordinary least squares (OLS) regression when the underlying assumptions of the OLS fail and therefore has been used by many researchers. Several measures for goodness of fit of Gini regression were suggested in the literature. However, to...

Under minimal assumptions finite sample confidence bands for quantile regression models can be constructed. These confidence bands are based on the "conditional pivotal property" of estimating equations that quantile regression methods aim to solve and will provide valid finite sample inference...

Regressions often use pre-orthogonalized regressors. For example, the exposure of a stock's return to exchange-rate changes is conventionally estimated by regression, and often, the market return is included as an additional regressor. By first orthogonalizing the market return on the exchange...