방법 비교

	ARIMA (Autoregressive Integrated Moving Average) 모형	최소제곱법(OLS) 회귀	벡터 오차 수정 모형 (VECM)
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	2015	2019	1987
창시자	Box & Jenkins (Box-Jenkins methodology)	Wooldridge (textbook treatment); classical least squares	Engle & Granger
유형	Univariate time-series model	Linear regression	Multivariate time-series model
원전	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.
별칭	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)
관련	5	5	4
요약	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.