Max-sum tests for cross-sectional dependence of high-demensional panel data

TL;DR

This paper introduces a new max-sum test for cross-sectional dependence in high-dimensional panel data, outperforming existing tests and providing new theoretical insights into sample correlation behaviors.

Contribution

The paper proposes the max-sum test, a novel method that balances sparse and non-sparse residual scenarios, and proves two conjectures on sample correlation sums, advancing theoretical understanding.

Findings

Max-sum test outperforms sum and max tests in simulations.

Theoretical proofs settle two longstanding conjectures.

The method shows robustness and higher power in empirical studies.

Abstract

We consider a testing problem for cross-sectional dependence for high-dimensional panel data, where the number of cross-sectional units is potentially much larger than the number of observations. The cross-sectional dependence is described through a linear regression model. We study three tests named the sum test, the max test and the max-sum test, where the latter two are new. The sum test is initially proposed by Breusch and Pagan (1980). We design the max and sum tests for sparse and non-sparse residuals in the linear regressions, respectively.And the max-sum test is devised to compromise both situations on the residuals. Indeed, our simulation shows that the max-sum test outperforms the previous two tests. This makes the max-sum test very useful in practice where sparsity or not for a set of data is usually vague. Towards the theoretical analysis of the three tests, we have settled two conjectures regarding the sum of squares of sample correlation coefficients asked by Pesaran (2004 and 2008). In addition, we establish the asymptotic theory for maxima of sample correlations coefficients appeared in the linear regression model for panel data, which is also the first successful attempt to our knowledge. To study the max-sum test, we create a novel method to show asymptotic independence between maxima and sums of dependent random variables. We expect the method itself is useful for other problems of this nature. Finally, an extensive simulation study as well as a case study are carried out. They demonstrate advantages of our proposed methods in terms of both empirical powers and robustness for residuals regardless of sparsity or not.