Parametric estimation of long memory in factor models

Yunus Emre Ergemen

Journal of Econometrics, 2023, vol. 235, issue 2, 1483-1499

Abstract: A dynamic factor model is proposed in that factor dynamics are driven by stochastic time trends describing arbitrary persistence levels. The proposed model is essentially a long memory factor model, which nests standard I(0) and I(1) behavior smoothly in common factors. In the estimation, principal components analysis (PCA) and conditional sum of squares (CSS) estimations are employed. For the dynamic model parameters, centered normal asymptotics are established at the usual parametric rates, and their small-sample properties are explored via Monte-Carlo experiments. The method is then applied to a panel of U.S. industry realized volatilities.

Keywords: Factor models; Long memory; Conditional sum of squares; Principal components analysis; Realized volatility (search for similar items in EconPapers)
JEL-codes: C12 C13 C33 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1483-1499

DOI: 10.1016/j.jeconom.2022.11.005

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