Forecasting economic time series using score-driven dynamic models with mixed-data sampling

Abstract

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of a low-frequency time series variable through the use of timely information from high-frequency variables. We aim to verify in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation. In particular, we forecast monthly inflation using daily oil prices and quarterly inflation using effective federal funds rates. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of point and density forecasts.

Nowcasting Economic Activity in Times of COVID-19: An Approximation from the Google Community Mobility Report

“…In this exercise the dimensionality of variables is not a big concern, therefore, the parametric methods embedded in the second group are adequate. Meanwhile, we select the MIDAS approach for nowcasting the industrial production growth rate, which performs significantly better when using DFM compared to the PCA methods, see Gorgi, Koopman and Mengheng (2018).…”

Mixed‐frequency predictive regressions with parameter learning

“…The first domain is the mixed-frequency econometric analysis. Popular frequentist approaches are mixed-data sampling models (MIDAS); see Ghysels et al (2004), Valle e Azevedo et al (2006), Ghysels et al (2007), Creal et al (2014), Blasques et al (2016), Gorgi et al (2019), and so forth. This paper contributes to Bayesian approaches, including Schorfheide and Song (2015), Schorfheide et al (2018), and Leippold and Yang (2019).…”

Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach

“…Gorgi et al (2019) propose a MIDAS-GAS model for economic time series like inflation or GDP growth, where a low-frequency economic variable is to be forecasted based on high-frequency financial information using a weighted sum of the high-frequency GAS innovations. This setting differs from our MF-GAS approach and the high-frequency financial data perspective: the MIDAS-GAS of Gorgi et al (2019) models mixed frequency data using latent GAS-driven components at the lower frequency, while the MF-GAS considers low-and high-frequency GAS components in order to model data, which is observed at a single frequency. Our intraday copula approach also shows some similarities to recent work of Koopman et al (2018).…”