Modelling Mixed-Frequency Time Series with Structural Change

Adrian Matthew G. Glova and Erniel Barrios ()
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Adrian Matthew G. Glova: University of the Philippines Diliman

Computational Economics, 2025, vol. 65, issue 6, No 6, 3237-3258

Abstract: Abstract Predictive ability of time series models is easily compromised in the presence of structural breaks, common among financial and economic variables amidst market shocks and policy regime shifts. We address this problem by estimating a semiparametric mixed-frequency model, that incorporate high frequency data either in the conditional mean or the conditional variance equation. The inclusion of high frequency data through non-parametric smoothing functions complements the low frequency data to capture possible non-linear relationships triggered by the structural change. Simulation studies indicate that in the presence of structural change, the varying frequency in the mean model provides improved in-sample fit and superior out-of-sample predictive ability relative to low frequency time series models. These hold across a broad range of simulation settings, such as varying time series lengths, nature of structural break points, and temporal dependencies. We illustrate the relative advantage of the method in predicting stock returns and foreign exchange rates in the case of the Philippines.

Keywords: Mixed frequency data; Structural break; Semiparametric models; Generalized additive models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10672-8

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