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Latviešu valodas versija nav pieejama

Elisa Tosetti

16 October 2009
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.
JEL Code
C10 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→General
C31 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions, Social Interaction Models
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models