Mixed frequency models: Bayesian approaches to estimation and prediction
AbstractWe describe Bayesian models for economic and financial time series that use regressors sampled at higher frequencies than the outcome of interest. The models are developed within the framework of dynamic linear models, which provides a high level of flexibility and allows direct interpretation of the results. The problem of the collinearity of intraperiod observations is solved using model selection and model averaging approaches. Bayesian approaches to model selection automatically adjust for multiple comparisons, while predictions based on model averaging allow us to account for both model and parameter uncertainty when predicting future observations. A novel aspect of the models presented here is the introduction of new formulations for the prior distribution on the model space that allow us to favor sparse models where the significant coefficients cluster on adjacent lags of the high frequency predictor. We illustrate our approach by predicting the gross national product of the United States using the term structure of interest rates.
Download InfoIf 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 26 (2010)
Issue (Month): 2 (April)
Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast
Mixed frequency data Model selection Model averaging Interest rates Gross national product;
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.:
- Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-57, July.
- Estrella, Arturo & Hardouvelis, Gikas A, 1991.
" The Term Structure as a Predictor of Real Economic Activity,"
Journal of Finance,
American Finance Association, vol. 46(2), pages 555-76, June.
- Arturo Estrella & Gikas A. Hardouvelis, 1989. "The term structure as a predictor of real economic activity," Research Paper 8907, Federal Reserve Bank of New York.
- Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, vol. 88(351), pages 549-63, September.
- Shen, Chung-Hua, 1996. "Forecasting macroeconomic variables using data of different periodicities," International Journal of Forecasting, Elsevier, vol. 12(2), pages 269-282, June.
- Uhlig, H.F.H.V.S., 1996.
"Bayesian Vector Autoregressions with Stochastic Volatility,"
1996-09, Tilburg University, Center for Economic Research.
- Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004.
"Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies,"
CIRANO Working Papers
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
- Birchenhall, Chris R, et al, 1999. "Predicting U.S. Business-Cycle Regimes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 313-23, July.
- Eric Ghysels & Jonathan H. Wright, 2006. "Forecasting professional forecasters," Finance and Economics Discussion Series 2006-10, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- Julia Campos & David F. Hendry & Hans-Martin Krolzig, 2003. "Consistent Model Selection by an Automatic "Gets" Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 803-819, December.
- Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2013.
"Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility,"
CEPR Discussion Papers
9312, C.E.P.R. Discussion Papers.
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Paper 1227, Federal Reserve Bank of Cleveland.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
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.