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Exploiting the monthly data-flow in structural forecasting

Author

Listed:
  • Domenico Giannone

    (Libera Università Internazionale degli Studi Sociali Guido Carli (LUISS)
    Centre for Economic Policy Research (CEPR))

  • Francesca Monti

    (Bank of England
    Centre for Macroeconomics (CFM))

  • Lucrezia Reichlin

    (London Business School (LBS)
    Centre for Economic Policy Research (CEPR))

Abstract

This 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.

Suggested Citation

  • Domenico Giannone & Francesca Monti & Lucrezia Reichlin, 2014. "Exploiting the monthly data-flow in structural forecasting," Discussion Papers 1416, Centre for Macroeconomics (CFM).
    • Handle: RePEc:cfm:wpaper:1416
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    References listed on IDEAS

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Hey, Economist! How Do You Forecast the Present?
      by Blog Author in Liberty Street Economics on 2017-06-16 20:15:00
    2. Exploiting the monthly data flow in structural forecasting
      by Christian Zimmermann in NEP-DGE blog on 2014-10-05 22:06:38

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    Cited by:

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    2. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    3. David Kohns & Arnab Bhattacharjee, 2020. "Developments on the Bayesian Structural Time Series Model: Trending Growth," Papers 2011.00938, arXiv.org.
    4. Boriss Siliverstovs, 2020. "Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts," Empirical Economics, Springer, vol. 58(1), pages 7-27, January.
    5. Jack Fosten & Daniel Gutknecht, 0. "Horizon confidence sets," Empirical Economics, Springer, vol. 0, pages 1-26.
    6. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.
    7. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2020. "Nowcasting with large Bayesian vector autoregressions," Working Paper Series 2453, European Central Bank.
    8. Christensen, Bent Jesper & Posch, Olaf & van der Wel, Michel, 2016. "Estimating dynamic equilibrium models using mixed frequency macro and financial data," Journal of Econometrics, Elsevier, vol. 194(1), pages 116-137.
    9. Norberto Rodríguez-Niño & Alejandra Ramírez-Ramírez, 2018. "Metodologías semi-estructurales para estimar la Inflación básica mensual en Colombia," Borradores de Economia 1040, Banco de la Republica de Colombia.

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    More about this item

    Keywords

    DSGE Models; Forecasting; Temporal Aggregation; Mixed Frequency Data; Large Datasets;
    All these keywords.

    JEL classification:

    • 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)

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