Cointegration Analysis with Mixed-Frequency Data

By Byeongchan Seong, Sung K. Ahn, Peter A. Zadrozny. Social Science Research Network, 2007

68 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3aa93787831c3ab1db1b2ea40bd162e75f41fea4

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