השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל פורייה-GARCH× | מודל ARCH (Autoregressive Conditional Heteroskedasticity)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 2000–2012 | 1982 |
| הוגה השיטה≠ | Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework | Robert F. Engle |
| סוג≠ | Volatility model | Conditional volatility model |
| מקור מכונן≠ | Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| כינויים | Fourier GARCH, Fourier-flexible GARCH, GARCH with Fourier terms, smooth-break GARCH | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| קשורות≠ | 5 | 6 |
| תקציר≠ | The Fourier GARCH model embeds trigonometric Fourier terms into a standard GARCH framework to capture smooth, gradual shifts in the conditional variance process without requiring knowledge of exact structural break dates. By approximating unknown break patterns with sinusoidal functions, it jointly models volatility clustering and time-varying unconditional variance. | 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. |
| ScholarGateמערך נתונים ↗ |
|
|