This paper proposes two consistent model selection procedures for factor-augmented regressions in finite samples. We first demonstrate that the usual cross-validation is inconsistent, but that a generalization, leave-d-out cross-validation, selects the smallest basis for the space spanned by the...

Long-horizon regression tests are widely used in empirical finance, despite evidence of severe size distortions. This paper introduces a new bootstrap method for small-sample inference in long-horizon regressions. A Monte Carlo study shows that this bootstrap test has much smaller size...

In this paper, we discuss and compare empirically various ways of computing multistep quantile forecasts of demand, with a special emphasis on the use of the quantile regression methodology. Such forecasts constitute a basis for production planning and inventory management in logistic systems...

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...

We consider testing for correct specification of a nonparametric instrumental variable regression. In this ill-posed inverse problem setting, the test statistic is based on the empirical minimum distance criterion corresponding to the conditional moment restriction evaluated with a Tikhonov...

Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is...