Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth
Many macroeconomic series such as US real output growth are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS approach is compared to other ways of making use of monthly data to predict quarterly output growth. The MIDAS specification used in the comparison employs a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way of exploiting monthly data compared to alternative methods. We also exploit the best method to use the monthly vintages of the indicators for real-time forecasting.
|Date of creation:||Oct 2007|
|Date of revision:|
|Contact details of provider:|| Postal: London E1 4NS|
Phone: +44 (0) 20 7882 5096
Fax: +44 (0) 20 8983 3580
Web page: http://www.econ.qmul.ac.uk
More information through EDIRC
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.:
- Croushore, Dean & Stark, Tom, 2001.
"A real-time data set for macroeconomists,"
Journal of Econometrics,
Elsevier, vol. 105(1), pages 111-130, November.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
- 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.).
- Joutz, Fred & Stekler, H. O., 2000. "An evaluation of the predictions of the Federal Reserve," International Journal of Forecasting, Elsevier, vol. 16(1), pages 17-38.
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2002.
"The use and abuse of 'real-time' data in economic forecasting,"
2001-015, Federal Reserve Bank of St. Louis.
- Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," Working Papers 0004, Federal Reserve Bank of Dallas.
- West, K.D., 1994.
"Asymptotic Inference About Predictive Ability,"
9417, Wisconsin Madison - Social Systems.
- James H. Stock & Mark W. Watson, 2003. "How did leading indicator forecasts perform during the 2001 recession?," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 71-90.
- 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.
- Jean Boivin & Serena Ng, 2005.
"Understanding and Comparing Factor-Based Forecasts,"
NBER Working Papers
11285, National Bureau of Economic Research, Inc.
- Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
- Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
- West, Kenneth D & McCracken, Michael W, 1998.
"Regression-Based Tests of Predictive Ability,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-40, November.
- West, K.D. & McCracken, M.W., 1997. "Regression-Based Tests of Predictive Ability," Working papers 9710, Wisconsin Madison - Social Systems.
- Kenneth D. West & Michael W. McCracken, 1998. "Regression-Based Tests of Predictive Ability," NBER Technical Working Papers 0226, National Bureau of Economic Research, Inc.
- Tom Stark and Dean Croushore, 2001.
"Forecasting with a Real-Time Data Set for Macroeconomists,"
Computing in Economics and Finance 2001
258, Society for Computational Economics.
- Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
- Tom Stark & Dean Croushore, 2001. "Forecasting with a real-time data set for macroeconomists," Working Papers 01-10, Federal Reserve Bank of Philadelphia.
- Schumacher, Christian & Breitung, Jörg, 2006. "Real-time forecasting of GDP based on a large factor model with monthly and quarterly data," Discussion Paper Series 1: Economic Studies 2006,33, Deutsche Bundesbank, Research Centre.
- Isabel Yi Zheng & James Rossiter, 2006. "Using Monthly Indicators to Predict Quarterly GDP," Staff Working Papers 06-26, Bank of Canada.
- Karamouzis, Nicholas & Lombra, Raymond, 1989.
"Federal reserve policymaking: an overview and analysis of the policy process,"
Carnegie-Rochester Conference Series on Public Policy,
Elsevier, vol. 30(1), pages 7-62, January.
- Karamouzis, N. & Lombra, R., 1988. "Federal Reserve Policy Making: An Overview And Analysis Of The Policy Process," Papers 0-88-8, Pennsylvania State - Department of Economics.
- Karamouzis, N. & Lombra, R., 1989. "Federal Reserve Policymaking: An Overview And Analisys Of The Policy Process," Papers 8-88-5, Pennsylvania State - Department of Economics.
- Athanasios Orphanides and Simon van Norden, 2001. "The Reliability of Inflation Forecasts Based on Output Gaps in Real Time," Computing in Economics and Finance 2001 247, Society for Computational Economics.
When requesting a correction, please mention this item's handle: RePEc:qmw:qmwecw:wp616. 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: (Nick Vriend)
If references are entirely missing, you can add them using this form.