Weak and Strong Cross Section Dependence and Estimation of Large Panels

By A. Chudik, M. Pesaran, Elisa Tosetti. Social Science Research Network, 2011

739 citations recorded by the source corpus.

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain 'granularity' conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/c097c23407403eb50969474a670f203e8736f096

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In Economics and Econometrics

Citations in the corpus

Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors (2015, neutral): “This assumption plays a similar role as Assumption 4.6 in Chudik, Pesaran, and Tosetti (2011) and ensures that π̂i, π̂MG and their asymptotic distributions are well defined.”
Estimating Panel Time-Series Models with Heterogeneous Slopes (2012, background): “The CCEMG approach is robust to the presence of a limited number of ‘strong’ factors as well as an infinite number of ‘weak’ factors – the latter can be associated with local spillover effects, whereas the former can represent global shocks such as the recent global financial crisis (Chudik et al. 2”
Econometric Analysis of High Dimensional VARs Featuring a Dominant Unit (2013, positive): “In the case where such dependencies are weak (in the sense formalized by Chudik et al., 2011), CP establish that the IVAR model de-couples into separate individual regressions that can be estimated consistently.”
Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model (2010, background): “Furthermore Pesaran and Tosetti (2009) and Chudik et al. (2009) extend this approach to spatial processes.”