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A Coincident Index, Common Factors, and Monthly Real GDP

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  • Roberto S. Mariano
  • Yasutomo Murasawa

Abstract

The Stock-Watson coincident index and its subsequent extensions assume a static linear one-factor model for the component indicators. This restrictive assumption is unnecessary if one defines a coincident index as an estimate of monthly real gross domestic products (GDP). This paper estimates Gaussian vector autoregression (VAR) and factor models for latent monthly real GDP and other coincident indicators using the observable mixed-frequency series. For maximum likelihood estimation of a VAR model, the expectation-maximization (EM) algorithm helps in finding a good starting value for a quasi-Newton method. The smoothed estimate of latent monthly real GDP is a natural extension of the Stock-Watson coincident index. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2009.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 72 (2010)
Issue (Month): 1 (02)
Pages: 27-46

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Handle: RePEc:bla:obuest:v:72:y:2010:i:1:p:27-46

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Cited by:
  1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
  2. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," Economics Working Papers ECO2009/32, European University Institute.
  3. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.
  4. Camacho, Maximo, 2013. "Mixed-frequency VAR models with Markov-switching dynamics," Economics Letters, Elsevier, vol. 121(3), pages 369-373.
  5. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
  6. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
  7. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.
  8. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.

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