Abstract
We introduce a new class of nonlinear autoregressive models from their representation as linear autoregressive models with time-varying coefficients. The parameter updating scheme is subsequently based on the score of the predictive likelihood function at each point in time. We study in detail the information theoretic optimality properties of this updating scheme and establish the asymptotic theory for the maximum likelihood estimator of the static parameters of the model. We compare the dynamic properties of the new model with those of well-known nonlinear dynamic models such as the threshold and smooth transition autoregressive models. Finally, we study the model’s performance in a Monte Carlo study and in an empirical out-of-sample forecasting analysis for U.S. macroeconomic time series.
Original language | English |
---|---|
Pages (from-to) | 559-578 |
Number of pages | 20 |
Journal | Econometric Reviews |
Volume | 39 |
Issue number | 6 |
Early online date | 31 Dec 2019 |
DOIs | |
Publication status | Published - 2 Jul 2020 |
Funding
Blasques and Lucas thank the Dutch National Science Foundation (NWO; grant VICI453-09-005) for financial support. Koopman acknowledges support from CREATES, Aarhus University, Denmark; it is funded by Danish National Research Foundation, (DNRF78). We thank Howell Tong and Timo Ter?svirta for helpful comments and suggestions.
Keywords
- Macroeconomic time series
- Score driven time-varying parameter models
- Smooth transition
- Treshold autoregressive models