Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Нелинейная модель ARMA (NARMA)× | Модель ARCH (авторегрессионная условная гетероскедастичность)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980s–1990s | 1982 |
| Автор метода≠ | Tong (1990); Granger & Terasvirta (1993) | Robert F. Engle |
| Тип≠ | Nonlinear time series model | Conditional volatility model |
| Основополагающий источник≠ | Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198522300 | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| Другие названия | NARMA, nonlinear ARMA, NLARMA, nonlinear autoregressive moving average | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Связанные≠ | 2 | 6 |
| Сводка≠ | The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial time series. | The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering. |
| ScholarGateНабор данных ↗ |
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