Panels with Nonstationary Multifactor Error Structures

By George Kapetanios, M. Pesaran, Takashi Yamagata. Social Science Research Network, 2006

1,040 citations recorded by the source corpus.

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/3fed42de4c498697514c30e201bea09b60279972

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Citations in the corpus

Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors (2015, neutral): “…be robust to an unknown number of unobserved common factors (Pesaran, 2006, and Chudik, Pesaran, and Tosetti, 2011), possible unit roots in factors (Kapetanios, Pesaran, and Yagamata, 2011), serial correlation of unknown form in ε∗it (Pesaran, 2006), spatial or other forms of weak cross-sectional…”
Public debt and growth: Heterogeneity and non-linearity (2015, neutral): “14 The simple algebraic mechanics of accounting for the unobservable factors f with cross-section averages of all variables is provided in Eberhardt and Teal (2013b), the asymptotic theory in Pesaran (2006) and Kapetanios et al. (2011).”
Cross-Sectional Dependence in Panel Data Analysis (2012, background): “Kapetanios, Pesaran, and Yamagata (2009) have extended the results of Pesaran (2006) by allowing unobserved common factors to follow unit root processes.”
Commodity Price Volatility and the Sources of Growth (2012, positive): “We assume that the error term, uit, has the following multi-factor error structure: uit = 0 ift + "it (16) where ft is a vector of unobserved common shocks, which can be stationary or nonstationary, see Kapetanios et al. (2010). The individual-specic errors, "it, are distributed independently acros”
Multilateral Resistance to Migration (2013, neutral): “…are nearly unchanged with respect to our baseline results in Table 2 as the estimator is robust to the presence of unit roots (Pesaran and Tosetti, 2011; Kapetanios, Pesaran, and Yamagata, 2011), while the FE estimates are not, which is shown on the positive coefficient on GDP per capita at origin.”
Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses (2013, positive): “Disregarding this crosssectional correlation in panel data models can lead to inconsistent parameter estimates and incorrect inference (Kapetanios et al. 2011).”