Porovnat metody

	ARFIMA: Model frakcionálně integrovaných ARMA	Panel Vector Autoregression (Panel VAR)	Kvantilová regrese
Obor	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model
Rok vzniku	1980	1988	1978
Tvůrce	Granger & Joyeux (1980); Hosking (1981)	Holtz-Eakin, Newey & Rosen	Koenker & Bassett
Typ	Long-memory time series model	Panel vector autoregression	Conditional quantile regression
Původní zdroj	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.	Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Další názvy	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	PVAR, panel vector autoregression, Panel VAR (PVAR)	conditional quantile regression, regression quantiles, Kantil Regresyon
Příbuzné	5	3	5
Shrnutí	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.	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.	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.