विधियों की तुलना करें

	दीर्घ-स्मृति मॉडल (ARFIMA, FIGARCH)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	वित्त	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1980	2019
प्रवर्तक	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)	Wooldridge (textbook treatment); classical least squares
प्रकार	Fractionally integrated time series model	Linear regression
मौलिक स्रोत	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
उपनाम	ARFIMA, FIGARCH, fractionally integrated models, fractional integration	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	4	5
सारांश	Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.	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).