Módszerek összehasonlítása

	MIDAS Regresszió: Előrejelzés vegyes frekvenciájú adatokon keresztül	ARIMA (Autoregressive Integrated Moving Average) modell
Tudományterület	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model
Keletkezés éve	2007	2015
Megalkotó	Eric Ghysels, Arthur Sinko & Rossen Valkanov	Box & Jenkins (Box-Jenkins methodology)
Típus	Parametric mixed-frequency forecasting model	Univariate time-series model
Alapmű	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
Alternatív nevek	Mixed Frequency Regression, Mixed Data Sampling Model, High-Frequency Forecasting Regression, MIDAS Regresyonu	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Kapcsolódó	3	5
Összefoglaló	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).