Vertaile menetelmiä

	ARFIMA: Murskattu integroitu ARMA-malli	OLS-regressio (Ordinary Least Squares)
Tieteenala	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1980	2019
Kehittäjä	Granger & Joyeux (1980); Hosking (1981)	Wooldridge (textbook treatment); classical least squares
Tyyppi	Long-memory time series model	Linear regression
Alkuperäislähde	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	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
Liittyvät	5	5
Tiivistelmä	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).