Econometric Methods

Diego Romero-Ávila
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Diego Romero-Ávila: Universidad Pablo de Olavide

Chapter Chapter 6 in Long-run Convergence in Greenhouse Gases, Reactive Compounds, Aerosol Precursors and Aerosols, 2025, pp 97-114 from Springer

Abstract: Abstract This chapter presents the econometric methodology for the construction of the large battery of panel unit root and stationarity tests used in the first part of the empirical analysis, which explicitly controls for cross-sectional dependence. Smith et al. (Journal of Applied Econometrics, 19, 147–170, 2004) and Hadri (The Econometrics Journal, 3, 148–161, 2000) control for general forms of cross-dependence through bootstrap methods, Breitung and Das (Statistica Neerlandica, 59, 414–433, 2005) control for contemporaneous cross-correlation through SUR, Chang (Journal of Econometrics, 110, 261–292, 2002) controls for cross-correlation through a methodology based on nonlinear instrumental variables methods, Choi (Combination unit root tests for cross-sectionally correlated panels. In D. Corbae, S. N. Durlauf, & B. E. Hansen (Eds.), Econometric theory and practice: Frontiers of analysis and applied research: Essays in honor of Peter C. B. Phillips. Cambridge University Press, pp 311–334, 2002) employs a restrictive one-factor model in which all cross-sectional units are equally affected by the common factor, Pesaran (Journal of Applied Econometrics, 22(2), 265–312, 2007b) also includes one common factor but with different factor loadings across units, and Moon and Perron (Journal of Econometrics, 122, 81–126, 2004) and Harris et al. (Journal of Business and Economic Statistics, 23, 395–409, 2005) incorporate multiple common factors. In the second part of this chapter, we present the main advantages of the PANIC approach versus other panel unit root and stationarity tests. First, PANIC allows for strong forms of cross-sectional dependence in the data such as cross-cointegration. Second, it decomposes the observed series into a common and an idiosyncratic component, thereby determining the source of nonstationarity in the observed series, that is, whether it stems from the common factor(s) and/or the idiosyncratic components. Third, PANIC is sufficiently flexible to allow for a different order of integration in both components, as opposed to other factor-based panel unit root tests. Fourth, PANIC serves as a cointegration framework applied to the log of the respective per capita emissions series, thus relaxing the homogeneity assumption previously imposed when focusing on relative series.

Keywords: Panel unit root tests; Panel stationarity tests; Cross-sectional dependence; Bootstrap distribution; Factor models; Nonlinear instrumental variables (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-031-81440-2_6

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DOI: 10.1007/978-3-031-81440-2_6

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