We study hypotheses testing in the presence of a possibly singular covariance matrix. We propose an alternative way to handle possible non-regularity in a covariance matrix of a Wald test, using the identity matrix as the weighting matrix when calculating the quadratic form. The resulting test...

Most panel data studies of the predictability of returns presume that the cross-sectional units are independent, an assumption that is not realistic. As a response to this, the current paper develops block bootstrap-based panel predictability tests that are valid under very general conditions....

This paper introduces the notion of nowcasting causality for mixed-frequency VARs as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in the mixed-frequency VAR setting of Ghysels 2012 and illustrate that nowcasting...

In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels 2012, where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

The paper compares the cointegration methods of Johansen and Bierens by means of simulations and a real world example. Drawing on the fact developed in a companion paper that the Johansen procedure has robustness properties against ARMA systems and the Bierens procedure is designed for ARMA...

In systems of variables with a specified or already identified cointegrating rank, stationarity of component variates can be tested by a simple restriction test. The implied decision is often in conflict with the outcome of unit root tests on the same variables. Using a framework of Bayes...

Bivariate time series data often show strong relationships between the two components, while both individual variables can be approximated by random walks in the short run and are obviously bounded in the long run. Three model classes are considered for a time-series model selection problem:...