Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Smooth Transition Autoregressive (STAR) Model× | ARFIMA: Model met fractioneel geïntegreerde ARMA× | Gewone Kleinste Kwadraten (GKK) Regressie× | |
|---|---|---|---|
| Vakgebied | Econometrie | Econometrie | Econometrie |
| Familie | Regression model | Regression model | Regression model |
| Jaar van ontstaan≠ | 1994 | 1980 | 2019 |
| Grondlegger≠ | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) | Granger & Joyeux (1980); Hosking (1981) | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Nonlinear time-series regime-switching model | Long-memory time series model | Linear regression |
| Oorspronkelijke bron≠ | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliassen≠ | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR | fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Verwant≠ | 4 | 5 | 5 |
| Samenvatting≠ | The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations. | ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly. | 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). |
| ScholarGateGegevensset ↗ |
|
|
|