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Exploiting the monthly data-flow in structural forecasting

Author

Listed:
  • Giannone, Domenico
  • Monti, Francesca
  • Reichlin, Lucrezia

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 andWouters (2011). Results show that the augmented monthly model does best for now-casting.

Suggested Citation

  • Giannone, Domenico & Monti, Francesca & Reichlin, Lucrezia, 2014. "Exploiting the monthly data-flow in structural forecasting," LSE Research Online Documents on Economics 57998, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:57998
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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

    Citations

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    Cited by:

    1. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    2. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    3. 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.
    4. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    5. Meyer-Gohde, Alexander & Shabalina, Ekaterina, 2022. "Estimation and forecasting using mixed-frequency DSGE models," IMFS Working Paper Series 175, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    6. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    7. 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.
    8. 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.
    9. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    10. 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.
    11. Alexander Eliseev, 2025. "Nowcasting Russian GDP in a Mixed-Frequency DSGE Model with a Panel of Non-Modelled Variables," Russian Journal of Money and Finance, Bank of Russia, vol. 84(3), pages 63-93, September.
    12. Hilde C. Bjornland & Jamie L. Cross & Felix Kapfhammer, 2023. "The Drivers of Emission Reductions in the European Carbon Market," CAMA Working Papers 2023-53, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2023. "A Bayesian DSGE Approach to Modelling Cryptocurrency"," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 1012-1035, December.
    14. 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.
    15. Agostino Consolo & Claudia Foroni & Catalina Martínez Hernández, 2023. "A Mixed Frequency BVAR for the Euro Area Labour Market," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 1048-1082, October.

    More about this item

    Keywords

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    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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