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The MIDAS Touch: Mixed Data Sampling Regression Models

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Author Info
Eric Ghysels ()
Pedro Santa-Clara
Rossen Valkanov

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

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Chernov, Mikhail & Gallant, A. Ronald & Ghysels, Eric & Tauchen, George, 2002. "Alternative Models for Stock Price Dynamic," Working Papers 02-03, Duke University, Department of Economics. [Downloadable!]
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  2. Carrasco, Marine & Florens, Jean-Pierre, 2000. "Generalization Of Gmm To A Continuum Of Moment Conditions," Econometric Theory, Cambridge University Press, vol. 16(06), pages 797-834, December. [Downloadable!]
  3. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July. [Downloadable!] (restricted)
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  4. Sims, Christopher A, 1971. "Discrete Approximations to Continuous Time Distributed Lags in Econometrics," Econometrica, Econometric Society, vol. 39(3), pages 545-63, May. [Downloadable!] (restricted)
  5. Drost, Feike C & Nijman, Theo E, 1993. "Temporal Aggregation of GARCH Processes," Econometrica, Econometric Society, vol. 61(4), pages 909-27, July. [Downloadable!] (restricted)
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  6. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November. [Downloadable!] (restricted)
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  7. Robinson, Peter M., 1977. "The construction and estimation of continuous time models and discrete approximations in econometrics," Journal of Econometrics, Elsevier, vol. 6(2), pages 173-197, September. [Downloadable!] (restricted)
  8. Geweke, John F, 1978. "Temporal Aggregation in the Multiple Regression Model," Econometrica, Econometric Society, vol. 46(3), pages 643-61, May. [Downloadable!] (restricted)
  9. Lars Peter Hansen & Thomas J. Sargent, 1981. "The dimensionality of the aliasing problem in models with rational spectral densities," Staff Report 72, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  10. Carrasco, Marine & Chernov, Mikhaël & Florens, Jean-Pierre & Ghysels, Eric, 2000. "Efficient Estimation of Jump Diffusions and General Dynamic Models with a Continuum of Moment Conditions," IDEI Working Papers 116, Institut d'Économie Industrielle (IDEI), Toulouse, revised 2002. [Downloadable!]
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  11. McCrorie, J. Roderick, 2000. "Deriving The Exact Discrete Analog Of A Continuous Time System," Econometric Theory, Cambridge University Press, vol. 16(06), pages 998-1015, December. [Downloadable!]
  12. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, 06. [Downloadable!] (restricted)
  13. Bierens, Herman J, 1990. "A Consistent Conditional Moment Test of Functional Form," Econometrica, Econometric Society, vol. 58(6), pages 1443-58, November. [Downloadable!] (restricted)
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  14. Drost, Feike C. & Werker, Bas J. M., 1996. "Closing the GARCH gap: Continuous time GARCH modeling," Journal of Econometrics, Elsevier, vol. 74(1), pages 31-57, September. [Downloadable!] (restricted)
  15. repec:cup:etheor:v:12:y:1996:i:2:p:215-56 is not listed on IDEAS
  16. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
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  17. Phillips, P C B, 1974. "The Estimation of Some Continuous Time Models," Econometrica, Econometric Society, vol. 42(5), pages 803-23, September. [Downloadable!] (restricted)
  18. Peter C.B. Phillips & Binbin Guo & Zhijie Xiao, 2002. "Efficient Regression in Time Series Partial Linear Models," Cowles Foundation Discussion Papers 1363, Cowles Foundation, Yale University. [Downloadable!]
  19. Phillips, P C B, 1972. "The Structural Estimation of a Stochastic Differential Equation System," Econometrica, Econometric Society, vol. 40(6), pages 1021-41, November. [Downloadable!] (restricted)
  20. Stinchcombe, Maxwell B. & White, Halbert, 1998. "Consistent Specification Testing With Nuisance Parameters Present Only Under The Alternative," Econometric Theory, Cambridge University Press, vol. 14(03), pages 295-325, June. [Downloadable!]
  21. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics," Econometrica, Econometric Society, vol. 72(3), pages 885-925, 05. [Downloadable!] (restricted)
  22. repec:cup:etheor:v:7:y:1991:i:4:p:531-42 is not listed on IDEAS
  23. Andreou, Elena & Ghysels, Eric, 2002. "Rolling-Sample Volatility Estimators: Some New Theoretical, Simulation, and Empirical Results," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 363-76, July.
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  24. Chambers, Marcus J., 1991. "Discrete Models for Estimating General Linear Continuous Time Systems," Econometric Theory, Cambridge University Press, vol. 7(04), pages 531-542, December. [Downloadable!]
  25. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," NBER Working Papers 10913, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  26. Comte, F. & Renault, E., 1996. "Noncausality in Continuous Time Models," Econometric Theory, Cambridge University Press, vol. 12(02), pages 215-256, June. [Downloadable!]
  27. Chacko, George & Viceira, Luis M., 2003. "Spectral GMM estimation of continuous-time processes," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 259-292. [Downloadable!] (restricted)
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions, Second Version," PIER Working Paper Archive 08-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Apr 2008. [Downloadable!]
  2. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO. [Downloadable!]
    Other versions:
  3. Eric Ghysels & Jonathan H. Wright, 2006. "Forecasting professional forecasters," Finance and Economics Discussion Series 2006-10, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  4. Ángel León & Juan Nave & Gonzalo Rubio, 2005. "The Relationship between Risk and Expected Return in Europe," DFAEII Working Papers 200508, University of the Basque Country - Department of Foundations of Economic Analysis II, revised 04 Jul 2006. [Downloadable!]
  5. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics. [Downloadable!]
  6. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2004. "A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1," NBER Working Papers 10447, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  7. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany. [Downloadable!]
  8. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics. [Downloadable!]
  9. Gregory H. Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility
    ," Working Papers 07-20, Bank of Canada. [Downloadable!]
  10. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2003. "There is a Risk-Return Tradeoff After All," CIRANO Working Papers 2003s-26, CIRANO. [Downloadable!]
    Other versions:
  11. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions," PIER Working Paper Archive 07-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
    Other versions:
  12. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," Economics Working Papers ECO2009/32, European University Institute. [Downloadable!]
  13. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies. [Downloadable!]
    Other versions:
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