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Predicting quarterly aggregates with monthly indicators

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  • Winkelried, Diego

    (Central Reserve Bank of Peru)

Abstract

Many important macroeconomic variables measuring the state of the economy are sampled quarterly and with publication lags, although potentially useful predictors are observed at a higher frequency almost in real time. This situation poses the challenge of how to best use the available data to infer the state of the economy. This paper explores the merits of the so-called Mixed Data Sampling (MIDAS) approach that directly exploits the information content of monthly indicators to predict quarterly Peruvian macroeconomic aggregates. To this end, we propose a simple extension, based on the notion of smoothness priors in a distributed lag model, that weakens the restrictions the traditional MIDAS approach imposes on the data to achieve parsimony. We also discuss the workings of an averaging scheme that combines predictions coming from non-nested specifications. It is found that the MIDAS approach is able to timely identify, from monthly information, important signals of the dynamics of the quarterly aggregates. Thus, it can deliver significant gains in prediction accuracy, compared to the performance of competing models that use exclusively quarterly information.

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Bibliographic Info

Paper provided by Banco Central de Reserva del Perú in its series Working Papers with number 2012-023.

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Date of creation: Dec 2012
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Handle: RePEc:rbp:wpaper:2012-023

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Related research

Keywords: Mixed-frequency data; MIDAS; model averaging; nowcasting; backcasting;

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References

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  1. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
  2. Evans, Martin D.D., 2005. "Where Are We Now? Real-Time Estimates of the Macro Economy," CEPR Discussion Papers 5270, C.E.P.R. Discussion Papers.
  3. George Kapetanios & Vincent Labhard & Simon Price, 2005. "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Bank of England working papers 268, Bank of England.
  4. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
  5. Domenico Giannone & Lucrezia Reichlin & David Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
  6. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank, Research Centre.
  7. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
  8. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  9. Ullah, Aman & Raj, Baldev, 1979. "A distributed lag estimator derived from Shiller's smoothness priors : An extension," Economics Letters, Elsevier, vol. 2(3), pages 219-223.
  10. Shiller, Robert J, 1973. "A Distributed Lag Estimator Derived from Smoothness Priors," Econometrica, Econometric Society, vol. 41(4), pages 775-88, July.
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Cited by:
  1. Winkelried, Diego, 2013. "Modelo de Proyección Trimestral del BCRP: Actualización y novedades," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 9-60.

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