Predicting Recessions; A New Approach for Identifying Leading Indicators and Forecast Combinations
This study proposes a data-based algorithm to select a subset of indicators from a large data set with a focus on forecasting recessions. The algorithm selects leading indicators of recessions based on the forecast encompassing principle and combines the forecasts. An application to U.S. data shows that forecasts obtained from the algorithm are consistently among the best in a large comparative forecasting exercise at various forecasting horizons. In addition, the selected indicators are reasonable and consistent with the standard leading indicators followed by many observers of business cycles. The suggested algorithm has several advantages, including wide applicability and objective variable selection.
|Date of creation:||01 Oct 2011|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (202) 623-7000
Fax: (202) 623-4661
Web page: http://www.imf.org/external/pubind.htmEmail:
More information through EDIRC
|Order Information:||Web: http://www.imf.org/external/pubs/pubs/ord_info.htm|
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.:
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics,
Elsevier, vol. 132(1), pages 169-194, May.
- Prakash Loungani, 2000.
"How Accurate Are Private Sector Forecasts; Cross-Country Evidence From Consensus Forecasts of Output Growth,"
IMF Working Papers
00/77, International Monetary Fund.
- Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
- Massimiliano Marcellino & James Stock & Mark Watson, 2005.
"A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series,"
285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
- 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 4976, C.E.P.R. Discussion Papers.
- Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
- Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.
- Edward E. Leamer, 2007.
"Housing is the business cycle,"
Proceedings - Economic Policy Symposium - Jackson Hole,
Federal Reserve Bank of Kansas City, pages 149-233.
- James D. Hamilton, 2010.
"Calling Recessions in Real Time,"
NBER Working Papers
16162, National Bureau of Economic Research, Inc.
- Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-77, April.
When requesting a correction, please mention this item's handle: RePEc:imf:imfwpa:11/235. 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: (Jim Beardow)or (Hassan Zaidi)
If references are entirely missing, you can add them using this form.