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Finite sample performance of small versus large scale dynamic factor models

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  • Alvarez, Rocio
  • Camacho, Maximo
  • Pérez-Quirós, Gabriel

Abstract

We examine the finite-sample performance of small versus large scale dynamic factor models. Our Monte Carlo analysis reveals that small scale factor models out-perform large scale models in factor estimation and forecasting for high levels of cross-correlation across the idiosyncratic errors of series belonging to the same category, for oversampled categories and, especially, for high persistence in either the common factor series or the idiosyncratic errors. Using a panel of 147 US economic indicators, which are classified into 13 economic categories, we show that a small scale dynamic factor model that uses one representative indicator of each category yields satisfactory or even better forecasting results than a large scale dynamic factor model that uses all the economic indicator

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8867.

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Date of creation: Mar 2012
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Handle: RePEc:cpr:ceprdp:8867

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Keywords: business cycles; output growth; time series;

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Cited by:
  1. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," MPRA Paper 53699, University Library of Munich, Germany.
  2. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank, Research Department.
  3. 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.
  4. Maximo Camacho & Rafael Domenech, 2010. "MICA-BBVA: A Factor Model of Economic and Financial Indicators for Short-term GDP Forecasting," Working Papers 1021, BBVA Bank, Economic Research Department.

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