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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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    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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    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. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    4. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
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    7. 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.
    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. Bhattacharjee, Arnab & Kohns, David, 2022. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," National Institute of Economic and Social Research (NIESR) Discussion Papers 538, National Institute of Economic and Social Research.
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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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