Forecasting business-cycle turning points with (relatively large) linear systems in real time
AbstractThe detection of business-cycle turning points is usually performed with non-linear discrete-regime models such as binary dependent variable (e.g., probit or logit) or Markov-switching methods. The probit model has the drawback that the continuous underlying target variable is discretized, with a considerable loss of information. The Markov-switching approach in general presupposes a non-linear data-generating process, and the numerical likelihood maximization becomes increasingly dif cult when more covariates are used. To avoid these problems we suggest to rst use standard linear systems (subset VARs with zero restrictions) to forecast the relevant underlying variable(s), and in a second step to derive the probability of a suitably de ned turning point from the forecast probability density function. This approach will never fail numerically. We also discuss and show how this approach can be used in real time in the presence of publication lags and to capture features of the data revision process, and we apply the method to German data; the event of the recent Great Recession is rst signalled in June 2008, several months before the of cial published data con rms it (but due to publication and recognition lags it is found after it already began in reality). --
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.
Bibliographic InfoPaper provided by Verein für Socialpolitik / German Economic Association in its series Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order with number 79709.
Date of creation: 2013
Date of revision:
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-02-02 (All new papers)
- NEP-ETS-2014-02-02 (Econometric Time Series)
- NEP-FOR-2014-02-02 (Forecasting)
- NEP-MAC-2014-02-02 (Macroeconomics)
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.:
- Bańbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2008.
"Large Bayesian VARs,"
Working Paper Series
0966, European Central Bank.
- Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
- Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
- Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
- Domenico Giannone & Martha Banbura & Lucrezia Reichlin, 2008. "Bayesian VARs with large panels," ULB Institutional Repository 2013/13388, ULB -- Universite Libre de Bruxelles.
- Jeremy J. Nalewaik, 2012. "Estimating Probabilities of Recession in Real Time Using GDP and GDI," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 235-253, 02.
- Österholm, Pär, 2012. "The limited usefulness of macroeconomic Bayesian VARs when estimating the probability of a US recession," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 76-86.
- Don Harding & Adrian Pagan, 2000.
"Disecting the Cycle: A Methodological Investigation,"
Econometric Society World Congress 2000 Contributed Papers
1164, Econometric Society.
- Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
- James D. Hamilton, 2010.
"Calling Recessions in Real Time,"
NBER Working Papers
16162, National Bureau of Economic Research, Inc.
- Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
- Daniel Detzer & Christian R. Proaño & Katja Rietzler & Sven Schreiber & Thomas Theobald & Sabine Stephan, 2012. "Verfahren der konjunkturellen Wendepunktbestimmung unter Berücksichtigung der Echtzeit-Problematik," IMK Studies 27-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
- Ng, Eric C.Y., 2012. "Forecasting US recessions with various risk factors and dynamic probit models," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 112-125.
- Layton, Allan P. & Katsuura, Masaki, 2001. "Comparison of regime switching, probit and logit models in dating and forecasting US business cycles," International Journal of Forecasting, Elsevier, vol. 17(3), pages 403-417.
- James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.