Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель АРХ (Авторегресивна умовна гетероскедастичність)× | Модель ARMA (авторегресійна ковзна середня)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1982 | 1970 |
| Автор методу≠ | Robert F. Engle | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Conditional volatility model | Time series model |
| Основоположне джерело≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Інші назви | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | 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. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateНабір даних ↗ |
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