Cointegration Analysis with Mixed-Frequency Data
We develop a method for directly modeling cointegrated multivariate time series that are observed in mixed frequencies. We regard lower-frequency data as regularly (or irregularly) missing and treat them with higher-frequency data by adopting a state-space model. This utilizes the structure of multivariate data as well as the available sample information more fully than the methods of transformation to a single frequency, and enables us to estimate parameters including cointegrating vectors and the missing observations of low-frequency data and to construct forecasts for future values. For the maximum likelihood estimation of the parameters in the model, we use an expectation maximization algorithm based on the state-space representation of the error correction model. The statistical efficiency of the developed method is investigated through a Monte Carlo study. We apply the method to a mixed-frequency data set that consists of the quarterly real gross domestic product and the monthly consumer price index.
|Date of creation:||2007|
|Date of revision:|
|Contact details of provider:|| Postal: Poschingerstrasse 5, 81679 Munich|
Phone: +49 (89) 9224-0
Fax: +49 (89) 985369
Web page: http://www.cesifo-group.de
More information through EDIRC
References listed on IDEAS
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.:
- Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-36, January.
- 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.
- Ben S. Bernanke & Mark Gertler & Mark Watson, 1997.
"Systematic Monetary Policy and the Effects of Oil Price Shocks,"
Brookings Papers on Economic Activity,
Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
- Bernanke, Ben S. & Gertler, Mark & Waston, Mark, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Working Papers 97-25, C.V. Starr Center for Applied Economics, New York University.
- Pons, Gabriel & Sans , Andreu, 2005. "Estimation Of Cointegrating Vectors With Time Series Measured At Different Periodicity," Econometric Theory, Cambridge University Press, vol. 21(04), pages 735-756, August.
- Engle, Robert & Granger, Clive, 2015.
"Co-integration and error correction: Representation, estimation, and testing,"
Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
- Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
- Nicolas A. Cuche & Martin K. Hess, 1999. "Estimating Monthly GDP In A General Kalman Filter Framework: Evidence From Switzerland," Working Papers 99.02, Swiss National Bank, Study Center Gerzensee.
- 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.
- Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo Group Munich.
- 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.
- 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.
- Granger, C. W. J. & Siklos, Pierre L., 1995.
"Systematic sampling, temporal aggregation, seasonal adjustment, and cointegration theory and evidence,"
Journal of Econometrics,
Elsevier, vol. 66(1-2), pages 357-369.
- Granger, C.W.J. & Siklos, P.L., 1993. "Systematic Sampling, Temporal Aggregation, Seasonal Adjustment, and Cointegration: Theory and Evidence," Working Papers 93001, Wilfrid Laurier University, Department of Economics.
- Haug, Alfred A, 2002. " Temporal Aggregation and the Power of Cointegration Tests: A Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 399-412, September.
- Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April.
- Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_1939. 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: (Klaus Wohlrabe)
If references are entirely missing, you can add them using this form.