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Forecasting with approximate dynamic factor models: The role of non-pervasive shocks

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  • Luciani, Matteo
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    Abstract

    This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 30 (2014)
    Issue (Month): 1 ()
    Pages: 20-29

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    Handle: RePEc:eee:intfor:v:30:y:2014:i:1:p:20-29

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Dynamic factor models; Penalized regressions; Local factors; Bayesian shrinkage; Forecasting;

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    Cited by:
    1. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.

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