Work

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Year: 2012

Type: article

Abstract: We study whether and when parameter-driven time-varying parameter models lead to forecasting gains over observation-driven models. We consider dynamic count, intensity, duration, volatility and copula models, including new specifications that have not been studied earlier in the literature. In an extensive Monte Carlo study, we find that observation-driven generalised autoregressive score (GAS) models have similar predictive accuracy to correctly specified parameter-driven models. In most cases, differences in mean squared errors are smaller than 1% and model confidence sets have low power when comparing these two alternatives. We also find that GAS models outperform many familiar observation-driven models in terms of forecasting accuracy. The results point to a class of observation-driven models with comparable forecasting ability to parameter-driven models, but lower computational complexity.

Source: The Review of Economics and Statistics

DOI: 10.1162/rest_a_00533

Authors: Siem Jan Koopman, André Lucas, Marcel Scharth

Institutions: Tinbergen Institute, Aarhus University, UNSW Sydney

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze