Work

Real‐Time Forecasting Using Mixed‐Frequency VARs With Time‐Varying Parameters

Year: 2025

Type: article

Abstract: ABSTRACT This paper provides a detailed assessment of the real‐time forecast accuracy of a wide range of vector autoregressive models that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed‐frequency time‐varying parameter vector autoregressive model with stochastic volatility. Monte Carlo simulation shows that the novel model is well‐suited to estimate missing monthly observations in an environment that is subject to parameter instability. In a real‐time forecast exercise, the model delivers accurate now‐ and forecasts and, on average, outperforms its competitors. Particularly, inflation and unemployment rate forecasts are more precise.

Source: Journal of Forecasting

DOI: 10.1002/for.3276

Authors: Markus Heinrich, Magnus Reif

Institutions: Christian-Albrechts-Universität zu Kiel, Deutsche Bundesbank

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,400

Funders: International Association for Applied Econometrics, Deutsche Bundesbank