Linganisha mbinu

	ARFIMA: Muundo wa Mfumo wa ARMA wenye Viwango vya Nusu	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)	Uchanganuzi wa Vector Autoregression wa Paneli (Panel VAR)	Regression ya Kiasi (Quantile Regression)
Nyanja	Ekonometriki	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model	Regression model
Mwaka wa asili	1980	2019	1988	1978
Mwanzilishi	Granger & Joyeux (1980); Hosking (1981)	Wooldridge (textbook treatment); classical least squares	Holtz-Eakin, Newey & Rosen	Koenker & Bassett
Aina	Long-memory time series model	Linear regression	Panel vector autoregression	Conditional quantile regression
Chanzo asilia	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Majina mbadala	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)	conditional quantile regression, regression quantiles, Kantil Regresyon
Zinazohusiana	5	5	3	5
Muhtasari	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.	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.