Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель АРХ (Авторегресивна умовна гетероскедастичність)× | Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1982 | 1970 |
| Автор методу≠ | Robert F. Engle | George Box and Gwilym Jenkins |
| Тип≠ | Conditional volatility model | Time series forecasting 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 | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Пов'язані | 6 | 6 |
| 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 ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateНабір даних ↗ |
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