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Parametric Estimation of Long Memory in Factor Models

Yunus Emre Ergemen ()
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Yunus Emre Ergemen: Aarhus University, Department of Economics and Business Economics, and CREATES, Postal: 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 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. JEL classifcation: C12, C13, C33 Key words: Factor models, long memory, conditional sum of squares, principal components analysis, realized volatility

Pages: 36
Date: 2022-06-24
New Economics Papers: this item is included in nep-dem, nep-ecm and nep-ets
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