Advanced Search
MyIDEAS: Login to save this paper or follow this series

Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility

Contents:

Author Info

  • Andrea Carriero
  • Todd E. Clark
  • Massimiliano Marcellino

Abstract

This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to GDP, we consider versions of the model with both constant variances and stochastic volatility. We also evaluate models with either constant or time-varying regression coefficients. We use Bayesian methods to estimate the model, in order to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of real-time GDP growth in the U.S. from 1985 through 2011. In terms of point forecasts, our proposal is comparable to alternative econometric methods and survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful, while parameter time-variation does not seem to matter.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.clevelandfed.org/research/workpaper/2012/wp1227.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1227.

as in new window
Length:
Date of creation: 2012
Date of revision:
Handle: RePEc:fip:fedcwp:1227

Contact details of provider:
Postal: 1455 East 6th St., Cleveland OH 44114
Phone: 216.579.2000
Web page: http://www.clevelandfed.org/
More information through EDIRC

Order Information:
Email:

Related research

Keywords: Bayesian statistical decision theory;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Clark, Todd E. & McCracken, Michael W., 2009. "Tests of Equal Predictive Ability With Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 27(4), pages 441-454.
  2. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank, Research Centre.
  3. Donald W.K. Andrews & Christopher J. Monahan, 1990. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 942, Cowles Foundation for Research in Economics, Yale University.
  4. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, Econometric Society, vol. 64(5), pages 1067-84, September.
  5. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers, C.E.P.R. Discussion Papers 8828, C.E.P.R. Discussion Papers.
  6. Geweke, John & Amisano, Gianni, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series, European Central Bank 0969, European Central Bank.
  7. Peter Christoffersen & Stefano Mazzotta, 2005. "The Accuracy of Density Forecasts from Foreign Exchange Options," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 578-605.
  8. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4976, C.E.P.R. Discussion Papers.
  9. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in Real-Time: A Density Combination Approach," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School 0003, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  10. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, Elsevier, vol. 105(1), pages 111-130, November.
  11. Banbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers, C.E.P.R. Discussion Papers 9112, C.E.P.R. Discussion Papers.
  12. Marcellino, Massimiliano & Porqueddu, Mario & Venditti, Fabrizio, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," CEPR Discussion Papers, C.E.P.R. Discussion Papers 9334, C.E.P.R. Discussion Papers.
  13. Bańbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Large Bayesian VARs," Working Paper Series, European Central Bank 0966, European Central Bank.
  14. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 529-542.
  15. 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, Federal Reserve Bank of Kansas City RWP 11-11, Federal Reserve Bank of Kansas City.
  16. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 69-87, January.
  17. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, Royal Statistical Society, vol. 174(2), pages 439-470, 04.
  18. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 13(3), pages 253-63, July.
  19. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 25, pages 177-190, April.
  20. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
  21. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series, European Central Bank 0633, European Central Bank.
  22. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  23. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Banco de Espa�a Working Papers 0807, Banco de Espa�a.
  24. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers, European University Institute ECO2008/16, European University Institute.
  25. Diron, Marie, 2006. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Working Paper Series, European Central Bank 0622, European Central Bank.
  26. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank, Research Centre.
  27. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 12(4), pages 413-17, October.
  28. David H. Romer & Christina D. Romer, 2000. "Federal Reserve Information and the Behavior of Interest Rates," American Economic Review, American Economic Association, American Economic Association, vol. 90(3), pages 429-457, June.
  29. D Agostino, Antonello & Gambetti, Luca & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," CEPR Discussion Papers, C.E.P.R. Discussion Papers 7542, C.E.P.R. Discussion Papers.
  30. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2012. "Selecting predictors by using Bayesian model averaging in bridge models," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 872, Bank of Italy, Economic Research and International Relations Area.
  31. Tom Stark & Dean Croushore, 2001. "Forecasting with a real-time data set for macroeconomists," Working Papers 01-10, Federal Reserve Bank of Philadelphia.
  32. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  33. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  34. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2012. "Common Drifting Volatility in Large Bayesian VARs," CEPR Discussion Papers, C.E.P.R. Discussion Papers 8894, C.E.P.R. Discussion Papers.
  35. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 7446, C.E.P.R. Discussion Papers.
  36. Guérin, Pierre & Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 8234, C.E.P.R. Discussion Papers.
  37. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, Elsevier, vol. 26(2), pages 293-311, April.
  38. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 27(4), pages 468-479.
  39. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers, C.E.P.R. Discussion Papers 8273, C.E.P.R. Discussion Papers.
  40. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers, Federal Reserve Bank of Minneapolis 274, Federal Reserve Bank of Minneapolis.
  41. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 546-554.
  42. Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 4(01), pages 108-124, April.
  43. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, Elsevier, vol. 20(3), pages 447-460.
  44. Giordani, Paolo & Villani, Mattias, 2009. "Forecasting Macroeconomic Time Series With Locally Adaptive Signal Extraction," Working Paper Series 234, Sveriges Riksbank (Central Bank of Sweden).
  45. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers, Czech National Bank, Research Department 2013/06, Czech National Bank, Research Department.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:fip:fedcwp:1227. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lee Faulhaber).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.