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Markov-switching dynamic factor models in real time

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  • Camacho, Maximo
  • Pérez-Quirós, Gabriel
  • Poncela, Pilar

Abstract

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefits of this extension and corroborate the results through several MonteCarlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8866.

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Date of creation: Feb 2012
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Handle: RePEc:cpr:ceprdp:8866

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Keywords: Business Cycles; Output Growth; Time Series;

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Cited by:
  1. Maximo Camacho & Marcos Dal Bianco & Jaime Martinez-Martin, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.
  2. 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.
  3. Gabriel Pérez-Quiros & Maximo Camacho & Pilar Poncela, 2010. "Green Shoots? Where, when and how?," Working Papers 2010-04, FEDEA.
  4. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.
  5. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.

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