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Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), [ 1 ] Ghysels, Sinko and Valkanov, [ 2 ] Andreou, Ghysels and Kourtellos (2010) [ 3 ] and Andreou ...
Ghysels' most recent research focuses on Mixed data sampling (MIDAS) regression models and filtering methods with applications in finance and other fields. He has also worked on diverse topics such as seasonality in economic times series, machine learning and AI applications in finance, quantum computing applications in finance, among many ...
MIDAS (Maximum Integration Data Acquisition System) has been developed as a general purpose data acquisition system for small and medium scale experiments originally by Stefan Ritt in 1993, followed by Pierre-André Amaudruz in 1996. It is written in C and published under the GPL.
A simple-to-implement, regression-based approach to nowcasting involves mixed-data sampling or MIDAS regressions. [18] The MIDAS regressions can also be combined with machine learning approaches. [19] Econometric models can improve accuracy. [20]
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In finance, MIDAS (an acronym for Market Interpretation/Data Analysis System) is an approach to technical analysis initiated in 1995 by the physicist and technical analyst Paul Levine, PhD, [1] and subsequently developed by Andrew Coles, PhD, and David Hawkins in a series of articles [2] and the book MIDAS Technical Analysis: A VWAP Approach to Trading and Investing in Today's Markets. [3]
MIDAS is mounted on the nadir panel of the Rosetta spacecraft and is composed of three main subsystems and an electronics box: [2] [3] [6] a funnel, shutter and collimator to control dust collection; a sample handling stage to collect and manipulate collected dust; an atomic force microscope.
Ooms, Marius (2009). "Trends in Applied Econometrics Software Development 1985–2008: An Analysis of Journal of Applied Econometrics Research Articles, Software Reviews, Data and Code". Palgrave Handbook of Econometrics. Vol. 2: Applied Econometrics. Palgrave Macmillan. pp. 1321– 1348. ISBN 978-1-4039-1800-0. Renfro, Charles G. (2004).