Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model
AbstractThe Stock--Watson coincident index and its subsequent extensions assume a static linear one-factor model for the component indicators. Such assumption is restrictive in practice, however, with as few as four indicators. In fact, such assumption is unnecessary if one poses the index construction problem as optimal prediction of latent monthly real GDP. This paper estimates a VAR model for latent monthly real GDP and other indicators using the observable mixed-frequency series. The EM algorithm is useful for overcoming the computational difficulty, especially in model selection. The smoothed estimate of latent monthly real GDP is the proposed index
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 710.
Date of creation: 11 Aug 2004
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state-space model; mixed-frequency; EM algorithm; monthly real GDP;
Other versions of this item:
- Roberto S. Mariano & Yasutomo Murasawa, 2004. "Constructing a Coincident Index of Business Cycles without Assuming a One-factor Model," Working Papers 22-2004, Singapore Management University, School of Economics, revised Oct 2004.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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.:
- Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998.
"Statistical Algorithms for Models in State Space Using SsfPack 2.2,"
1998-141, Tilburg University, Center for Economic Research.
- Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
- Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998. "Statistical algorithms for models in state space using SsfPack 2.2," Economics Series Working Papers 1998-W06, University of Oxford, Department of Economics.
- Stock, J.H. & Watson, M.W., 1989.
"New Indexes Of Coincident And Leading Economic Indicators,"
178d, Harvard - J.F. Kennedy School of Government.
- James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
- Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
- Francis X. Diebold & Glenn D. Rudebusch, 1994.
"Measuring Business Cycles: A Modern Perspective,"
NBER Working Papers
4643, National Bureau of Economic Research, Inc.
- Liu, H & Hall, Stephen G, 2001. "Creating High-Frequency National Accounts with State-Space Modelling: A Monte Carlo Experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 441-49, September.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
- Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
- Paul A. Ruud., 1988.
"Extensions of Estimation Methods Using the EM Algorithm.,"
Economics Working Papers
8899, University of California at Berkeley.
- Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
- Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
- Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
- Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, October.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Chow, Gregory C & Lin, An-loh, 1971.
"Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series,"
The Review of Economics and Statistics,
MIT Press, vol. 53(4), pages 372-75, November.
- Tom Doan, . "CHOWLIN: RATS procedure to distribute a series to a higher frequency using related series," Statistical Software Components RTS00036, Boston College Department of Economics.
- Tom Doan, . "DISAGGREGATE: RATS procedure to implement general disaggregation (interpolation/distribution) procedure," Statistical Software Components RTS00050, Boston College Department of Economics.
- Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
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