PANEL UNIT ROOT TESTS WITH CROSS-SECTION DEPENDENCE: A FURTHER INVESTIGATION

Jushan Bai · Serena Ng

240 citations0/0 files storedEconometric Theory

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Spatial and Panel Data Analysis Monetary Policy and Economic Impact Diverse Scientific and Economic Studies

Abstract

An effective way to control for cross-section correlation when conducting a panel unit root test is to remove the common factors from the data. However, there remain many ways to use the defactored residuals to construct a test. In this paper, we use the panel analysis of nonstationarity in idiosyncratic and common components (PANIC) residuals to form two new tests. One estimates the pooled autoregressive coefficient, and one simply uses a sample moment. We establish their large-sample properties using a joint limit theory. We find that when the pooled autoregressive root is estimated using data detrended by least squares, the tests have no power. This result holds regardless of how the data are defactored. All PANIC-based pooled tests have nontrivial power because of the way the linear trend is removed.

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References

1988Testing for a Unit Root in Time Series Regression 2002Unit root tests in panel data: asymptotic and finite-sample properties 2007A simple panel unit root test in the presence of cross‐section dependence 1999A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test 2002Determining the Number of Factors in Approximate Factor Models 2001LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power 1984Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order 2004A PANIC Attack on Unit Roots and Cointegration

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2009Panel Unit Root Tests in the Presence of Cross-Sectional Dependencies: Comparison and Implications for Modelling 2022Impact of energy efficiency on CO2 Emissions: Empirical evidence from developing countries 2015Panicca: Panic on Cross‐Section Averages 2010Cross-sectional dependence robust block bootstrap panel unit root tests 2013Cointegration in Panel Data with Structural Breaks and Cross-Section Dependence

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