Exploiting the monthly data-flow in structural forecasting
AbstractThis paper shows how and when it is possible to obtain a mapping from a quarterly DSGE model to a monthly specification that maintains the same economic restrictions and has real coefficients. We use this technique to derive the monthly counterpart of the Gali et al (2011) model. We then augment it with auxiliary macro indicators which, because of their timeliness, can be used to obtain a now-cast of the structural model. We show empirical results for the quarterly growth rate of GDP, the monthly unemployment rate and the welfare relevant output gap defined in Gali, Smets and Wouters (2011). Results show that the augmented monthly model does best for now-casting.
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Bibliographic InfoPaper provided by Centre for Macroeconomics (CFM) in its series Discussion Papers with number 1416.
Length: 23 pages
Date of creation: Jun 2014
Date of revision:
DSGEmodels; forecasting; temporal aggregation; mixed frequency data; large datasets;
Other versions of this item:
- Giannone, Domenico & Monti , Francesca & Reichlin , Lucrezia, 2014. "Exploiting the monthly data flow in structural forecasting," Bank of England working papers, Bank of England 509, Bank of England.
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-07-21 (All new papers)
- NEP-ECM-2014-07-21 (Econometrics)
- NEP-FOR-2014-07-21 (Forecasting)
- NEP-MAC-2014-07-21 (Macroeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jaimovich, Nir & Rebelo, Sérgio, 2006.
"Can News About the Future Drive the Business Cycle?,"
CEPR Discussion Papers, C.E.P.R. Discussion Papers
5877, C.E.P.R. Discussion Papers.
- Nir Jaimovich & Sergio Rebelo, 2009. "Can News about the Future Drive the Business Cycle?," American Economic Review, American Economic Association, American Economic Association, vol. 99(4), pages 1097-1118, September.
- Nir Jaimovich & Sergio Rebelo, 2006. "Can News About the Future Drive the Business Cycle?," 2006 Meeting Papers, Society for Economic Dynamics 31, Society for Economic Dynamics.
- Nir Jaimovich & Sergio Rebelo, 2006. "Can News About the Future Drive the Business Cycle?," NBER Working Papers 12537, National Bureau of Economic Research, Inc.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008.
"A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models,"
Working Papers ECARES, ULB -- Universite Libre de Bruxelles
2008_034, ULB -- Universite Libre de Bruxelles.
- Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
- Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series, European Central Bank 0674, European Central Bank.
- Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5724, C.E.P.R. Discussion Papers.
- Marianna Cervená & Martin Schneider, 2010. "Short-term forecasting GDP with a DSGE model augmented by monthly indicators," Working Papers, Oesterreichische Nationalbank (Austrian Central Bank) 163, Oesterreichische Nationalbank (Austrian Central Bank).
- Julio Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361 National Bureau of Economic Research, Inc.
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