Efficient estimation of factor models with time and cross-sectional dependence

Alexander Heinemann*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This paper studies the efficient estimation of large-dimensional factor models with both time and cross-sectional dependence assuming (N, T) separability of the covariance matrix. The asymptotic distribution of the estimator of the factor and factor-loading space under factor stationarity is derived and compared to that of the principal component (PC) estimator. The paper also considers the case when factors exhibit a unit root. We provide feasible estimators and show in a simulation study that they are more efficient than the PC estimator in finite samples. In application, the estimation procedure is employed to estimate the Lee-Carter model and life expectancy is forecast. The Dutch gender gap is explored and the relationship between life expectancy and the level of economic development is examined in a cross-country comparison.

Original languageEnglish
Pages (from-to)1107-1122
Number of pages16
JournalJournal of Applied Econometrics
Volume32
Issue number6
DOIs
Publication statusPublished - 2017

Keywords

  • LEE-CARTER MODEL
  • PRINCIPAL COMPONENTS
  • PANEL-DATA
  • MORTALITY
  • NUMBER

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