পদ্ধতির তুলনা করুন

	দীর্ঘ-স্মৃতি মডেল (ARFIMA, FIGARCH)	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	অর্থায়ন	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1980	2019
প্রবর্তক	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)	Wooldridge (textbook treatment); classical least squares
ধরন	Fractionally integrated time series model	Linear regression
মৌলিক উৎস	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
অপর নাম	ARFIMA, FIGARCH, fractionally integrated models, fractional integration	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	4	5
সারসংক্ষেপ	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).