Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustais ARCH modelis× | Autoregresīvās nosacītās heteroskedastiskuma (ARCH) modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2002–2008 | 1982 |
| Autors≠ | Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s | Robert F. Engle |
| Tips≠ | Volatility / conditional heteroscedasticity model | Conditional volatility model |
| Pirmavots | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| Citi nosaukumi | robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series. | 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. |
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