Сравнение на методи
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| Модел ARCH (Авторегресивен условен хетероскедастичност)× | Модел ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1982 | 2015 |
| Създател≠ | Robert F. Engle | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | Conditional volatility model | Univariate time-series model |
| Основополагащ източник≠ | 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., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Други названия≠ | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateНабор от данни ↗ |
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