- Tytuł:
- State-dependent Autoregressive Models with p Lags: Properties, Estimation and Forecasting
- Autorzy:
-
Gobbi, Fabio
Mulinacci, Sabrina - Powiązania:
- https://bibliotekanauki.pl/articles/2119921.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
convolution-based autoregressive models
level-increment dependence
nonlinear time series
maximum likelihood
forecasting accuracy - Opis:
- In this paper we consider a class of nonlinear autoregressive models in which a specific type of dependence structure between the error term and the lagged values of the state variable is assumed. We show that there exists an equivalent representation given by a p-th order state-dependent autoregressive (SDAR(p)) model where the error term is independent of the last p lagged values of the state variable (yt−1, . . . , yt−p) and the autoregressive coefficients are specific functions of them. We discuss a quasi-maximum likelihood estimator of the model parameters and we prove its consistency and asymptotic normality. To test the forecasting ability of the SDAR(p) model, we propose an empirical application to the quarterly Japan GDP growth rate which is a time series characterized by a level-increment dependence. A comparative analyses is conducted taking into consideration some alternative and competitive models for nonlinear time series such as SETAR and AR-GARCH models.
- Źródło:
-
Central European Journal of Economic Modelling and Econometrics; 2022, 1; 81-108
2080-0886
2080-119X - Pojawia się w:
- Central European Journal of Economic Modelling and Econometrics
- Dostawca treści:
- Biblioteka Nauki
Artykuł