手法を比較

	MIDAS回帰：混合データ頻度を跨いだ予測	ARIMA（自己回帰和分移動平均）モデル	ベクトル自己回帰（VAR）モデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2007	2015	2005
提唱者	Eric Ghysels, Arthur Sinko & Rossen Valkanov	Box & Jenkins (Box-Jenkins methodology)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
種類	Parametric mixed-frequency forecasting model	Univariate time-series model	Multivariate time-series model
原典	Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90.	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	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
別名	Mixed Frequency Regression, Mixed Data Sampling Model, High-Frequency Forecasting Regression, MIDAS Regresyonu	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
関連	3	5	4
概要	MIDAS (Mixed Data Sampling) Regression is an econometric framework that directly incorporates high-frequency predictors into models for lower-frequency outcome variables without requiring temporal aggregation of the regressors. Introduced by Eric Ghysels, Arthur Sinko, and Rossen Valkanov in 2007, MIDAS uses parsimoniously parameterized lag polynomials — such as the Beta or Exponential Almon weighting schemes — to summarize the information content of many high-frequency lags while avoiding parameter proliferation.	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).