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Mixed-data sampling

Known as: Mixed, Mixed data sampling 
Mixed-data sampling (MIDAS) is an econometric regression or filtering method developed by Ghysels et al. There is now a substantial literature on… 
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Related topics

Related topics

2 relations
Distributed lagKalman filter

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Forecasting gold futures market volatility using macroeconomic variables in the United States
2018
2018
What determines the long-term correlation between oil prices and exchange rates?
2017
2017
European equity market integration and joint relationship of conditional volatility and correlations
Highly Cited
2017
Highly Cited
2017
A multiple support vector machine approach to stock index forecasting with mixed frequency sampling
2017
2017
The Impact of Money Supply on Nigeria Economy: A Comparison of Mixed Data Sampling (MIDAS) and ARDL Approach
The study investigates the long and short run relationships between broad money supply and real aggregate output (GDP) in Nigeria… 
2011
2011
Forecasting correlations during the late-2000 s financial crisis : short-run component , long-run component , and structural breaks
We empirically investigate the predictive power of the various components affecting correlations that have been recently… 
2010
2010
Mixed Data Sampling
MIxed DAta Sampling (MIDAS) regression models were introduced in both filtering and regression context to deal with situations… 
Highly Cited
2008
Highly Cited
2008
Are There Structural Breaks in Realized Volatility
Constructed from high-frequency data, realized volatility (RV) provides an accurate estimate of the unobserved volatility of… 
2007
2007
Components of Market Risk and Return
This article proposes a flexible but parsimonious specification of the joint dynamics of market risk and return to produce… 
Review
2006
Review
2006
Forecasting Professional Forecasters
Surveys of forecasters, containing respondents’ predictions of future values of key macroeconomic variables, receive a lot of… 
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