Сравнение на методи
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| Обобщена авторегресионна условна хетероскедастичност (GARCH)× | Метод на най-малките квадрати (МНК)× | Сезонен ARIMA (SARIMA)× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1986 | 2019 | 2015 |
| Създател≠ | Tim Bollerslev | Wooldridge (textbook treatment); classical least squares | Box & Jenkins (seasonal extension of ARIMA) |
| Тип≠ | Conditional volatility model | Linear regression | Seasonal time-series model |
| Основополагащ източник≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | 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≠ | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. |
| ScholarGateНабор от данни ↗ |
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