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Mixed frequency structural models: estimation, and policy analysis

Author

Listed:
  • Claudia Foroni

    (Norges Bank (Central Bank of Norway))

  • Massimiliano Marcellino

    (European University Institute, Bocconi University and CEPR)

Abstract

In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identfication problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identfication issues, and yield more reliable policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.

Suggested Citation

  • Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.
    • Handle: RePEc:bno:worpap:2013_15
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    File URL: https://www.norges-bank.no/en/news-events/news-publications/Papers/Working-Papers/2013/WP-201315/
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    References listed on IDEAS

    as
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    4. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2011. "Estimating Dynamic Equilibrium Models using Macro and Financial Data," CREATES Research Papers 2011-21, Department of Economics and Business Economics, Aarhus University.
    5. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
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    7. Fuhrer, Jeffrey C. & Rudebusch, Glenn D., 2004. "Estimating the Euler equation for output," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1133-1153, September.
    8. ., 2008. "Population Aging, Financial Markets and Monetary Policy," Chapters, in: Dirk Broeders & Sylvester Eiffinger & Aerdt Houben (ed.), Frontiers in Pension Finance, chapter 11, Edward Elgar Publishing.
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    Cited by:

    1. Kristina Bluwstein & Fabio Canova, 2016. "Beggar-Thy-Neighbor? The International Effects of ECB Unconventional Monetary Policy Measures," International Journal of Central Banking, International Journal of Central Banking, vol. 12(3), pages 69-120, September.
    2. Giannone, Domenico & Monti, Francesca & Reichlin, Lucrezia, 2016. "Exploiting the monthly data flow in structural forecasting," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 201-215.
    3. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    4. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.

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    More about this item

    Keywords

    Structural VAR; DSGE models; temporal aggregation; mixed frequency data; estimation. policy analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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