The MIDAS Touch: Mixed Data Sampling Regression Models

Eric Ghysels, Pedro Santa-Clara and Rossen Valkanov

University of California at Los Angeles, Anderson Graduate School of Management from Anderson Graduate School of Management, UCLA

Abstract: We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

Date: 2004-06-22
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Working Paper: The MIDAS Touch: Mixed Data Sampling Regression Models (2004)
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