A homogeneous approach to testing for Granger non-causality in heterogeneous panels

Bibliographic Data

ID21542078
AuthorsArtūras Juodis (0000-0003-3973-7221, University of Amsterdam, corresponding author), Yiannis Karavias (0000-0002-1208-5537, University of Birmingham), Vasilis Sarafidis (0000-0001-6808-3947, BI Norwegian Business School)
Year2021
Volume60
Issue1
Pages93-112
Publication date2021-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEmpirical Economics (JOURNAL)
Journal identifiersISSN: 0377-7332 • E-ISSN: 1435-8921
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s00181-020-01970-9
OpenAlexW3109950613
LanguageEN
Citations received84
References cited34

This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional ( N ) and time series ( T ) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross sections guarantees that the estimator has a $$\sqrt{NT}$$ NT convergence rate. In order to account for the well-known “Nickell bias”, the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks’ profitability and cost efficiency

Algorithm · Econometrics · Estimator · Granger causality · Jackknife resampling · Null hypothesis · Panel data · Pooling · Statistical hypothesis testing · Statistics · Wald test · Computer Science · Energy, Environment, Economic Growth · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis · Artificial Intelligence