Cointegration Analysis with Mixed-Frequency Data
AbstractWe 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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Bibliographic InfoPaper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 1939.
Date of creation: 2007
Date of revision:
missing data; Kalman filter; expectation maximization algorithm; forecasting; error correction model; smoothing; maximum likelihood estimation;
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