Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25984
Authors: 
Seong, Byeongchan
Ahn, Sung K.
Zadrozny, Peter A.
Year of Publication: 
2007
Series/Report no.: 
CESifo working paper 1939
Abstract: 
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.
Subjects: 
missing data
Kalman filter
expectation maximization algorithm
forecasting
error correction model
smoothing
maximum likelihood estimation
JEL: 
C13
C22
C32
Document Type: 
Working Paper

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