Harding, Don (2008): Detecting and forecasting business cycle turning points.
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The R word has begun to appear in the media again bringing with it three technical questions viz, How will we know we are in recession? How will we know when it has ended? And How can we forecast its onset and ending? This paper does not provide answers to these questions rather it focuses on the technical issues that we need to resolve in order to provide good answers to these questions. The paper has three significant findings. First, the business cycle states obtained by the BBQ algorithm are complex statistical processes and it is not possible to write down an exact likelihood function for them. Second, for the classical and acceleration cycles it is possible to obtain a reasonably simple approximation to the BBQ algorithm that may permit one to write down a likelihood function. Third, when evaluating these algorithms there is a large di¤erence between the results using US GDP as compared to UK GDP or simulated data from models fit to US GDP. Specifically, turning points are much easier to detect in US GDP than in other series. One needs to take this into account when using US based research on detecting and forecasting business cycle turning points.
|Item Type:||MPRA Paper|
|Original Title:||Detecting and forecasting business cycle turning points|
|Keywords:||Business cycle; turning points; forecasting; peak; trough|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
|Depositing User:||Don Harding|
|Date Deposited:||22. Sep 2011 09:30|
|Last Modified:||30. Dec 2015 22:12|
Artis, M.J., Z.G., Kontolemis and D.R. Osborn (1997), "Business Cycles for G7 and European Countries". Journal of Business, 70, pp. 249-279.
Baxter, M. and R. King (1999), "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series", Review of Economics and Statistics, 81, pp. 575-593.
Birchenall, C.R., H. Jessen, D.R. Osborne and P. Simpson (1999), "Predicting U.S. Business-Cycle Regimes", Journal of Business and Economic Statistics, 17, 313-323.
Bry, G., Boschan, C., (1971), Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, New York, NBER.
Burns, A.F., Mitchell, W.C., (1946), Measuring Business Cycles, New York, NBER.
Burnside, C., (1998), "Detrending and Business Cycle Facts: A Comment". Journal of Monetary Economics, 41, pp. 513-532.
Christiano, L., Fitzgerald, T., (1998), "The Business Cycle: It's still a Puzzle". Federal Reserve Bank of Chicago Economic Perspectives, 4th Quarter, pp. 56-83.
Chin, D., Geweke, J., and P., Miller, (2000). "Predicting Turning Points, Federal Reserve Bank of Minneapolois Research Department Staff Report No. 267.
Clements, M.P. and H-M. Krolzig (2000), "Business Cycle Asymmetries: Characterisation and Testing Based on Markov Switching Autoregres sions", (mimeo, University of Warwick).
Cogley, T., (2001), "Alternative de�nitions of the business cycle and their implications for business cycle models: A reply to Torben Mark Pederson", Journal of Economic Dynamics & Control, 25 pp 1103-1107.
Diebold, F.X. and G. D. Rudebusch (2001), "Five Questions About Business Cycles", FRBSF Economic Review.
Durland, J.M., McCurdy, T.H., 1994, "Duration-Dependent Transitions in a Markov Model of US GNP Growth". Journal of Business and Economic Statistics, 12, pp. 279-288.
Estrella, A. and F.S. Mishkin (1998), "Predicting US Recessions: Financial Variables as Leading Indicators", Review of Economics and Statistics, LXXX, 28-61.
Fagan, G., Henry, J., and R., Mestre, (2001), "An Area-Wide-Model (AWM) for the Euro Area", European Central Bank Working Paper No. 42.
Hamilton, J.D., (1989), "A New Approach to the Economic Analysis of Non-Stationary Times Series and the Business Cycle", Econometrica, 57, pp. 357-384.
Hamilton, J.D., (1994) Time Series Analysis, Princton.
Harding, D., (2003), Essays on the Business Cycle, Phd Thesis Yale University.
Harding, D., (2008), The equivalence of several methods for extracting permanent and transitory components
Harding, D , Pagan, A.R., (2000a), "Knowing the Cycle", In: Backhouse, R., Salanti, A., (Eds.) Macroeconomics in the Real World (Oxford University Press)
Harding D., and A.R. Pagan, (2002), "Dissecting the Cycle: A methodological Investigation", Journal of Monetary Economics. 49 pages 365-381
Harding D., and A.R. Pagan, (2002), "A Comparison of Two Business Cycle Dating Methods", Journal of Economic Dynamics and Control, 27 pages 1681-1690
Harding D., and A.R. Pagan, (2006), "Synchronisation of Cycles", Journal of Econometrics.
Harding D., and A.R. Pagan, (2007), "Measurement of Business Cycles", New Palgrave
Harding D., and A.R. Pagan, (2007), "The Econometric Aanalysis of Some Constructed Binary Time series", Mimeo, University of Melbourne.
Harvey, A.C., and A. Jaeger (1993). "Detrending, Stylized Facts and the Business Cycle", Journal of Applied Econometrics, 8(3), July-Sept. pp 231-47.
Hodrick, R.J., and E.C., Prescott (1997),"Post-War U.S business cycles: A descriptive empirical investigation". Journal of Money Credit and Banking, 29 pp1_16.
Kedem, B., (1980), Binary Time Series, Marcel Dekker, New York
Krolzig, H-M and J. Toro (2000), "Classical and Modern Business Cycle Measurement: The European Case", Mimeo, University of Oxford
McQueen, G. and S. Thorley (1993), "Asymmetric Business Cycle Turning Points", Journal of Monetary Economics, 31, 341-362.
Thorpe, W. (1926), Business Annals, Monograph Number 8, NBER.