Authors: Federico Crudu (Department of Economics and Statistics, University of Siena), Giovanni Mellace (University of Southern Denmark), Zsolt Sándor (Sapientia Hungarian University of Transylvania)
Abstract: This paper introduces a class of jackknife-based test statistics for linear regression models with endogeneity and heteroskedasticity in the presence of many potentially weak instrumental variables. The tests may be used when considering hypotheses on the full parameter vector or hypotheses defined as linear restrictions. We show that in the limit and under the null the proposed statistics are distributed as a combination of chi squares but more familiar chi square limits may be derived. A simulation study shows the competitive power properties of most of the proposed tests.
Abstract: This paper introduces a class of jackknife-based test statistics for linear regression models with endogeneity and heteroskedasticity in the presence of many potentially weak instrumental variables. The tests may be used when considering hypotheses on the full parameter vector or hypotheses defined as linear restrictions. We show that in the limit and under the null the proposed statistics are distributed as a combination of chi squares but more familiar chi square limits may be derived. A simulation study shows the competitive power properties of most of the proposed tests.