Сравнение на методи

	ARFIMA: Модел с дробно интегрирани ARMA	Логистична регресия	Модел с фиксирани ефекти за панелни данни
Област	Иконометрия	Статистика за изследвания	Иконометрия
Семейство	Regression model	Process / pipeline	Regression model
Година на възникване	1980	1958	2014
Създател	Granger & Joyeux (1980); Hosking (1981)	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data
Тип	Long-memory time series model	Method	Panel data regression
Основополагащ източник	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Други названия	fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Свързани	5	3	5
Резюме	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).