Cointegration analysis with mixed-frequency data - Byeongchan Seong; Sung K. Ahn; Peter A. Zadrozny (Center for Economic Studies and ifo Institute (CESifo), 2007)
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.
Keywords: C13; C22; C32; 330; missing data; Kalman filter; expectation maximization algorithm; forecasting; error correction model; smoothing; maximum likelihood estimation; Kointegration; Zeitreihenanalyse; Zustandsraummodell; Prognoseverfahren; Fehlerkorrekturmodell; Theorie; Schätzung; Sozialprodukt; Lebenshaltungsindex; USA