Generalized Efficient Inference on Factor Models with Long-Range Dependence

Yunus Emre Ergemen ()
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Yunus Emre Ergemen: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: A dynamic factor model is considered that contains stochastic time trends allowing for stationary and nonstationary long-range dependence. The model nests standard I(0) and I(1) behaviour smoothly in common factors and residuals, removing the necessity of a priori unit-root and stationarity testing. Short-memory dynamics are allowed in the common factor structure and possibly heteroskedastic error term. In the estimation, a generalized version of the principal components (PC) approach is proposed to achieve efficiency. Asymptotics for efficient common factor and factor loading as well as long-range dependence parameter estimates are justified at standard parametric convergence rates. The use of the method for the selection of number of factors and testing for latent components is discussed. Finite-sample properties of the estimates are explored via Monte-Carlo experiments, and an empirical application to U.S. economy diffusion indices is included.

Keywords: Factor models; long-range dependence; principal components; efficiency; hypothesis testing (search for similar items in EconPapers)
JEL-codes: C12 C13 C33 (search for similar items in EconPapers)
Pages: 27
Date: 2016-01-29
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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