เปรียบเทียบวิธี

	แบบจำลอง Vector Autoregression (VAR)	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	แบบจำลองการแก้ไขข้อผิดพลาดเวกเตอร์ (VECM)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model	Regression model
ปีกำเนิด	2005	2015	2019	1987
ผู้ริเริ่ม	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Box & Jenkins (Box-Jenkins methodology)	Wooldridge (textbook treatment); classical least squares	Engle & Granger
ประเภท	Multivariate time-series model	Univariate time-series model	Linear regression	Multivariate time-series model
แหล่งต้นตำรับ	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
ชื่อเรียกอื่น	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)
ที่เกี่ยวข้อง	4	5	5	4
สรุป	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).	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).	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).	The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.