| Abstract: |
This paper proposes a mixed-frequency error-correction model in order to
develop a regressionapproach for non-stationary variables sampled at different
frequencies that are possiblycointegrated. We show that, at the model
representation level, the choice of the timing betweenthe low-frequency
ependent and the high-frequency explanatory variables to be included in
thelong-run has an impact on the remaining dynamics and on the forecasting
properties. Then, wecompare in a set of Monte Carlo experiments the
forecasting performances of the low-frequencyaggregated model and several
mixed-frequency regressions. In particular, we look at both theunrestricted
mixed-frequency model and at a more parsimonious MIDAS regression. Whilst
theexisting literature has only investigated the potential improvements of the
MIDAS framework forstationary time series, our study emphasizes the need to
include the relevant cointegratingvectors in the non-stationary case.
Furthermore, it is illustrated that the exact timing of thelong-run
relationship does notmatter as long as the short-run dynamics are adapted
according to the composition of thedisequilibrium error. Finally, the
unrestricted model is shown to suffer from parameterproliferation for small
sample sizeswhereas MIDAS forecasts are robust to over-parameterization.
Hence, the data-driven,low-dimensional and flexible weighting structure makes
MIDAS a robust and parsimonious method tofollow when the true underlying DGP
is unknown while still exploiting information present in thehigh-frequency. An
empirical application illustrates the theoretical and the Monte Carlo results. |