Mixed data sampling (MIDAS) is an econometric regression or filtering method developed by Ghysels et al. A simple regression example has the regressor appearing at a higher frequency than the regressand:
where y is the regressand, x is the regressor, m denotes the frequency - for instance if y is yearly 
is quarterly - ε is the disturbance and B(L1 / m;θ) is a lag distribution, for instance the Beta function or the Almon lag.
References
Eric Ghysels and J. Wright (2009), Forecasting Professional Forecasters, Journal of Business and Economic Statistics (forthcoming)
Anderson, E., Eric Ghysels and J. Juergens (2009) The Impact of Risk and Uncertainty on Expected Returns, Journal of Financial Economics (forthcoming)
Eric Ghysels and B. Sohn (2009) Which Power Variation Predicts Volatility Well? Journal of Empirical Finance, (forthcoming)
Andreou, E, Eric Ghysels and A. Kourtellos (2007) Regression Models With Mixed Sampling Frequencies, Journal of Econometrics (forthcoming)
Eric Ghysels, Santa-Clara, P. and Valkanov, R. (2005), There is a Risk-return Trade-off After All, Journal of Financial Economics, 76, 509-548.
Eric Ghysels, Santa-Clara, P. and Valkanov, R. (2006) Predicting volatility: How to get most out of returns data sampled at different frequencies Journal of Econometrics 131, 59-95
Eric Ghysels, Sinko, A., Valkanov, R. (2007) MIDAS Regressions: Further Results and New Directions. Econometric Reviews, 26 (1), 53–90
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