Published:
Springer Science and Business Media LLC, 2021
Published in:
Empirical Economics, 60 (2021) 1, Seite 93-112
Language:
English
DOI:
10.1007/s00181-020-01970-9
ISSN:
0377-7332;
1435-8921
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (<jats:italic>N</jats:italic>) and time series (<jats:italic>T</jats:italic>) 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 <jats:inline-formula><jats:alternatives><jats:tex-math>$$\sqrt{NT}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
<mml:msqrt>
<mml:mrow>
<mml:mi>NT</mml:mi>
</mml:mrow>
</mml:msqrt>
</mml:math></jats:alternatives></jats:inline-formula> 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.</jats:p>