قارن الطرق

	نموذج ARIMA (الانحدار الذاتي المتكامل للمتوسط المتحرك)	تحليل البنية الدقيقة للسوق والبيانات عالية التردد	انحدار المربعات الصغرى العادية (OLS)
المجال	الاقتصاد القياسي	التمويل	الاقتصاد القياسي
العائلة	Regression model	Regression model	Regression model
سنة النشأة	2015	2007	2019
صاحب الطريقة	Box & Jenkins (Box-Jenkins methodology)	Hasbrouck (2007); Aït-Sahalia & Jacod (2014)	Wooldridge (textbook treatment); classical least squares
النوع	Univariate time-series model	Market microstructure / high-frequency econometrics	Linear regression
المصدر التأسيسي	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
الأسماء البديلة	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ذات صلة	5	5	5
الملخص	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014).	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).