Detection of Units with Pervasive Effects in Large Panel Data Models

94 Pages Posted: 4 Dec 2018 Last revised: 26 Apr 2019

See all articles by George Kapetanios

George Kapetanios

King's College, London

M. Hashem Pesaran

University of Southern California - Department of Economics; University of Cambridge - Trinity College (Cambridge)

Simon Reese

USC Dornsife Institute for New Economic Thinking

Multiple version iconThere are 2 versions of this paper

Date Written: April 24, 2019

Abstract

The importance of units with pervasive impacts on a large number of other units in a network has become increasingly recognized in the literature. In this paper we propose a new method to detect such pervasive units by basing our analysis on unit-speci c residual error variances in the context of a standard factor model, subject to suitable adjustments due to multiple testing. Our proposed method allows us to estimate and identify pervasive units having neither a priori knowledge of the interconnections amongst cross-section units nor a short list of candidate units. It is applicable even if the cross section dimension exceeds the time dimension, and most importantly it could end up with none of the units selected as pervasive when this is in fact the case. The sequential multiple testing procedure proposed exhibits satisfactory small-sample performance in Monte Carlo simulations and compares well relative to existing approaches. We apply the proposed detection method to sectoral indices of US industrial production, US house price changes by states, and the rates of change of real GDP and real equity prices across the world's largest economies.

Keywords: Pervasive Units, Factor Models, Systemic Risk, Cross-Sectional Dependence

JEL Classification: C18, C23, C55

Suggested Citation

Kapetanios, George and Pesaran, M. Hashem and Reese, Simon, Detection of Units with Pervasive Effects in Large Panel Data Models (April 24, 2019). USC-INET Research Paper No. 19-09, Available at SSRN: https://ssrn.com/abstract=3293371 or http://dx.doi.org/10.2139/ssrn.3293371

George Kapetanios (Contact Author)

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

M. Hashem Pesaran

University of Southern California - Department of Economics

3620 South Vermont Ave. Kaprielian (KAP) Hall 300
Los Angeles, CA 90089
United States

University of Cambridge - Trinity College (Cambridge) ( email )

United Kingdom

Simon Reese

USC Dornsife Institute for New Economic Thinking ( email )

3620 S. Vermont Avenue, KAP 364F
Los Angeles, CA 90089-0253
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
70
Abstract Views
660
rank
319,804
PlumX Metrics