Compara mètodes

	ARFIMA: Model de l'ARMA amb integració fraccionària	Regressió per Mínims Quadrats Ordinàris (MQO)	Autoregressió vectorial de panell (Panel VAR)
Camp	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Any d'origen	1980	2019	1988
Autor original	Granger & Joyeux (1980); Hosking (1981)	Wooldridge (textbook treatment); classical least squares	Holtz-Eakin, Newey & Rosen
Tipus	Long-memory time series model	Linear regression	Panel vector autoregression
Font seminal	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	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.
Àlies	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	PVAR, panel vector autoregression, Panel VAR (PVAR)
Relacionats	5	5	3
Resum	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).	Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level.