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 multi...
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| Sprache: | eng |
| Veröffentlicht: |
Munich
Univ., Center for Economic Studies u.a.
2007
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| Schriftenreihe: | CESifo working paper series Empirical and theoretical methods
1939 |
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Tag hinzufügen | Zusammenfassung: | 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. |
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| Beschreibung: | Literaturverz. S. 30 - 33 |
| Beschreibung: | 33 S. graph. Darst., Tab. |