Testing a Large Set of Zero Restrictions in Regression Models, with an Application to Mixed Frequency Granger Causality

Abstract

This paper proposes a new test for a large set of zero restrictions in regression models based on a seemingly overlooked, but simple, dimension reduction technique. The procedure involves multiple parsimonious regression models where key regressors are split across simple regressions. Each parsimonious regression model has one key regressor and other regressors not associated with the null hypothesis. The test is based on the maximum of the squared parameters of the key regressors. Parsimony ensures sharper estimates and therefore improves power in small sample. We present the general theory of our test and focus on mixed frequency Granger causality as a prominent application involving many zero restrictions.

Keywords: dimension reduction, Granger causality test, max test, Mixed Data Sampling (MIDAS), parsimonious regression models

JEL Classification: C12, C22, C51