Panel Unit Root Tests in the Presence of a Multifactor Error Structure

By M. Pesaran, L. Smith, Takashi Yamagata. Social Science Research Network, 2013

351 citations recorded by the source corpus.

This paper extends the cross sectionally augmented panel unit root test proposed by Pesaran (2007) to the case of a multifactor error structure. The basic idea is to exploit information regarding the unobserved factors that are shared by other time series in addition to the variable under consideration. Importantly, our test procedure only requires specification of the maximum number of factors, in contrast to other panel unit root tests based on principal components that require in addition the estimation of the number of factors as well as the factors themselves. Small sample properties of the proposed test are investigated by Monte Carlo experiments, which suggest that it controls well for size in almost all cases, especially in the presence of serial correlation in the error term, contrary to alternative test statistics. Empirical applications to Fisher's inflation parity and real equity prices across different markets illustrate how the proposed test works in practice.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/1ef33a1153ebcecd73c76d3082ac081bf9e85f77

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Citations in the corpus

Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence (2016, background): “a is similar to the results in Phillips and Sul (2007), the only difference being that the data generating process of Ft is different.”
Renewable and Nonrenewable Energy Consumption, Economic Growth, and Emissions: International Evidence (2020, methodology): “These include the Pesaran (2007) and Pesaran, Smith and Yamagata (2013) panel unit root tests.”
Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors (2015, neutral): “In addition to the regressors included in the panel ARDL model, following Pesaran, Smith, and Yamagata (2013) we also assume that there exists a set of additional covariates, git, that are affected by the same set of unobserved common factors, ft.”
The Local Power of the CADF and CIPS Panel Unit Root Tests (2016, background): “The rejection frequencies for CADF are averaged across the cross-section. including tests for a unit root with multiple common factors, and cointegration (see, for example, Pesaran et al., 2013; Banerjee and Carrion-i-Silvestre, 2011).”
Does urbanization increase pollutant emission and energy intensity? evidence from some Asian developing economies (2019, methodology): “According to Pesaran et al. (2013), the proposed CIPS and CSB tests have the correct size for all combinations of the cross-section (N) and time series (T) dimensions considered, although the CSB test performs better than the CIPS test for smaller sample sizes.”
A Simple Panel Unit‐Root Test with Smooth Breaks in the Presence of a Multifactor Error Structure (2016, positive): “Following Pesaran et al. (2013), the required assumptions for deriving the null distri- bution of the ti(N , T ) statistic are given as follows: Assumption 1 (Idiosyncratic errors).”
Testing for Panel Cointegration Using Common Correlated Effects Estimators (2017, methodology): “…, which are the same elements that define the limiting distributions in Pesaran (2007) – see his Theorem 3.2 for the CADFif statistic when r D 1 – and Pesaran et al. (2013) – see their Theorem 2.1 when r > 1.5 Although the limiting distributions of the test statistics proposed in this article…”