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Real-time forecasting with a mixed-frequency VAR

Author Info

  • Frank Schorfheide
  • Dongho Song

Abstract

This paper develops a vector autoregression (VAR) for macroeconomic time series which are observed at mixed frequencies – quarterly and monthly. The mixed-frequency VAR is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. Using a real-time data set, we generate and evaluate forecasts from the mixed-frequency VAR and compare them to forecasts from a VAR that is estimated based on data time-aggregated to quarterly frequency. We document how information that becomes available within the quarter improves the forecasts in real time.

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Paper provided by Federal Reserve Bank of Minneapolis in its series Working Papers with number 701.

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Date of creation: 2012
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Handle: RePEc:fip:fedmwp:701
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Keywords: Bayesian statistical decision theory ; Forecasting ; Vector autoregression;

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Find related papers by JEL classification:
  • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
  • 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
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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  1. Daniel F. Waggoner & Tao Zha, 1998. "Conditional forecasts in dynamic multivariate models," FRB Atlanta Working Paper 98-22, Federal Reserve Bank of Atlanta.
  2. Bańbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Large Bayesian VARs," Working Paper Series 0966, European Central Bank.
  3. 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, April.
  4. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?," CEPR Discussion Papers 5829, C.E.P.R. Discussion Papers.
  5. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
  6. Marco Del Negro & Frank Schorfheide, 2002. "Priors from general equilibrium models for VARs," FRB Atlanta Working Paper 2002-14, Federal Reserve Bank of Atlanta.
  7. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  8. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
  9. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
  10. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
  11. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.
  12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2011. "Bayesian VARs: specification choices and forecast accuracy," Working Paper 1112, Federal Reserve Bank of Cleveland.
  13. Herbst, Edward & Schorfheide, Frank, 2012. "Evaluating DSGE model forecasts of comovements," Journal of Econometrics, Elsevier, vol. 171(2), pages 152-166.
  14. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, Elsevier.
  15. Kling, John L & Bessler, David A, 1989. "Calibration-Based Predictive Distributions: An Application of Prequential Analysis to Interest Rates, Money, Prices, and Output," The Journal of Business, University of Chicago Press, vol. 62(4), pages 477-99, October.
  16. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(3), pages 698-721.
  17. 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.
  18. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," FRB Atlanta Working Paper 96-13, Federal Reserve Bank of Atlanta.
  19. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.
  20. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2008. "A Monthly Indicator of the Euro Area GDP," Economics Working Papers ECO2008/32, European University Institute.
  21. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, December.
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RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography for Economics:
  1. > Econometrics > Forecasting > Nowcasting
  2. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
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