Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель нелінійної ARMA (NARMA)× | Модель АРХ (Авторегресивна умовна гетероскедастичність)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1980s–1990s | 1982 |
| Автор методу≠ | Tong (1990); Granger & Terasvirta (1993) | Robert F. Engle |
| Тип≠ | Nonlinear time series model | Conditional volatility model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | NARMA, nonlinear ARMA, NLARMA, nonlinear autoregressive moving average | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Пов'язані≠ | 2 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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