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요한센 공적분 검정 및 벡터 오차 수정 모형×ARIMA (Autoregressive Integrated Moving Average) 모형×Vector Autoregression (VAR) Model×
분야재무학계량경제학계량경제학
계열Regression modelRegression modelRegression model
기원 연도199120152005
창시자Søren JohansenBox & Jenkins (Box-Jenkins methodology)Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
유형Multivariate cointegration / vector error correction modelUnivariate time-series modelMultivariate time-series model
원전Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580. DOI ↗Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗
별칭Johansen test, VECM, vector error correction model, multivariate cointegrationBox-Jenkins model, ARIMA(p,d,q), ARIMA Modelivector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
관련354
요약The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.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).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).
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