Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель нелинейного скользящего среднего (NMA)× | Модель гладкого переходного авторегрессионного процесса (STAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1978 | 1994 |
| Автор метода≠ | Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory) | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) |
| Тип≠ | Nonlinear time series model | Nonlinear time-series regime-switching model |
| Основополагающий источник≠ | Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen. link ↗ | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ |
| Другие названия≠ | NMA model, nonlinear moving average, NLMA model, nonlinear MA | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR |
| Связанные | 4 | 4 |
| Сводка≠ | The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion. | The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations. |
| ScholarGateНабор данных ↗ |
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