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Exact Discrete Representations of Linear Continuous Time Models with Mixed Frequency Data

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  • Michael A. Thornton

Abstract

The time aggregation of vector linear processes containing (i) mixed stock‐flow data and (ii) aggregated at mixed frequencies, is explored, focusing on a method to translate the parameters of the underlying continuous time model into those of an equivalent model of the observed data. Based on manipulations of a general state‐space form, the results may be used to model multiple frequencies or aggregation schemes. Estimation of the continuous time parameters via the ARMA representation of the observable data vector is discussed and demonstrated in an application to model stock price and dividend data. Simulation evidence suggests that these estimators have superior properties to the traditional approach of concentrating the data to a single low frequency.

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  • Michael A. Thornton, 2019. "Exact Discrete Representations of Linear Continuous Time Models with Mixed Frequency Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 951-967, November.
    • Handle: RePEc:bla:jtsera:v:40:y:2019:i:6:p:951-967
    DOI: 10.1111/jtsa.12471
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    Cited by:

    1. Chambers, Marcus J., 2020. "Frequency domain estimation of cointegrating vectors with mixed frequency and mixed sample data," Journal of Econometrics, Elsevier, vol. 217(1), pages 140-160.
    2. Cuixia Jiang & Tingting Zhao & Qifa Xu & Dan Hu, 2024. "An unrestricted MIDAS ordered logit model with applications to credit ratings," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2722-2739, July.

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