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The MIDAS Touch: Mixed Data Sampling Regression Models

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  • Eric Ghysels

    ()

  • Pedro Santa-Clara
  • Rossen Valkanov

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 finance. Nous introduisons des modèles de régression MIDAS (Mixed Data Sampling). Ce sont des modèles de régression avec des séries temporelles échantillonées à différentes fréquences. Nous analysons les liens avec les modèles à retards échelonnés.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2004s-20.

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Date of creation: 01 May 2004
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Handle: RePEc:cir:cirwor:2004s-20

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Keywords: distributed log models; aliasing; discretization bias; retards échelonnés; aliasing; biais de discrétisation;

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