Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| ARFIMA: Fraksjonert Integrert ARMA-modell× | Minste kvadraters metode (OLS)× | Kvantilregresjon× | |
|---|---|---|---|
| Fagfelt | Økonometri | Økonometri | Økonometri |
| Familie | Regression model | Regression model | Regression model |
| Opprinnelsesår≠ | 1980 | 2019 | 1978 |
| Opphavsperson≠ | Granger & Joyeux (1980); Hosking (1981) | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Type≠ | Long-memory time series model | Linear regression | Conditional quantile regression |
| Opprinnelig kilde≠ | 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 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | 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 | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relaterte | 5 | 5 | 5 |
| Sammendrag≠ | 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). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateDatasett ↗ |
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