So sánh phương pháp

	Hồi quy MIDAS: Dự báo trên các Tần suất Dữ liệu Hỗn hợp	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Mô hình Tự hồi quy Vector (VAR)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model	Regression model
Năm ra đời	2007	2015	2005
Người khởi xướng	Eric Ghysels, Arthur Sinko & Rossen Valkanov	Box & Jenkins (Box-Jenkins methodology)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Loại	Parametric mixed-frequency forecasting model	Univariate time-series model	Multivariate time-series model
Công trình gốc	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.
Tên gọi khác	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
Liên quan	3	5	4
Tóm tắt	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).