השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל מיתוג-משטרים של מרקוב (MS-AR / MS-VAR)× | Exponential GARCH (EGARCH)× | רגרסיית ריבועים פחותים רגילים (OLS)× | |
|---|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model | Regression model |
| שנת המקור≠ | 1989 | 1991 | 2019 |
| הוגה השיטה≠ | Hamilton (1989); Kim & Nelson (1999) | Nelson | Wooldridge (textbook treatment); classical least squares |
| סוג≠ | Regime-switching time series model | Conditional volatility model (asymmetric GARCH variant) | Linear regression |
| מקור מכונן≠ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| כינויים≠ | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| קשורות≠ | 5 | 4 | 5 |
| תקציר≠ | The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions. | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. | 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). |
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