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

Arturas Juodis; Yiannis Karavias; Vasilis Sarafidis

doi:10.1007/s00181-020-01970-9

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

Arturas Juodis, Yiannis Karavias, Vasilis Sarafidis

Economics

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

190 Downloads (Pure)

Abstract

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 \(\sqrt{NT}\) 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.

Original language	English
Pages (from-to)	1-20
Journal	Empirical Economics
Volume	2020
DOIs	https://doi.org/10.1007/s00181-020-01970-9
Publication status	Published - 23 Nov 2020

Bibliographical note

Funding Information:
We are delighted to contribute this paper to a special issue in honour of Professor Badi Baltagi, who has made an enormous contribution to the field of econometrics. We are grateful to the Guest Editors and all referees involved. We would like to thank participants at the IPDC2019 conference (Vilnius) for useful comments and suggestions. Part of this research project was conducted while the first author was visiting the Applied Macroeconomic Research Division (TMTS) at the Bank of Lithuania (BoL). The views expressed in this paper are those of authors and do not necessarily represent the official views of the Bank of Lithuania or the Eurosystem. Financial support from the Netherlands Organization for Scientific Research (NWO) is gratefully acknowledged by Juodis. Sarafidis gratefully acknowledges financial support from the Australian Research Council, under research Grant No. DP-170103135.

Publisher Copyright:
© 2020, The Author(s).

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Bias correction
Fixed effects
Granger causality
Panel data
VAR
“Nickell bias”

ASJC Scopus subject areas

Statistics and Probability
Mathematics (miscellaneous)
Social Sciences (miscellaneous)
Economics and Econometrics

Access to Document

10.1007/s00181-020-01970-9Licence: Creative Commons: Attribution (CC BY)

JuodisA2020homogenous
Juodis, A., Karavias, Y. & Sarafidis, V. A homogeneous approach to testing for Granger non-causality in heterogeneous panels. Empir Econ (2020). https://doi.org/10.1007/s00181-020-01970-9
Final published version, 361 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "A homogeneous approach to testing for Granger non-causality in heterogeneous panels",

abstract = "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 \(\sqrt{NT}\) 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{\textquoteright} profitability and cost efficiency.",

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N2 - 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 \(\sqrt{NT}\) 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.

AB - 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 \(\sqrt{NT}\) 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.

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JF - Empirical Economics

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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