Mixed data sampling

Mixed data sampling

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:

y_t = \beta_0 + \beta_1 B(L^{1/m};\theta)x_t^{(m)} + \varepsilon_t^{(m)},\,

where y is the regressand, x is the regressor, m denotes the frequency – for instance if y is yearly x_t^{(4)} is quarterly – ε is the disturbance and B(L1 / m;θ) is a lag distribution, for instance the Beta function or the Almon lag.

The regression models can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data. Bai, Ghysels and Wright (2010) examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involve a system of equations, whereas in contrast MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors. In cases where the MIDAS regression is only an approximation, the approximation errors tend to be small.

See also

References

Andreou, E, Eric Ghysels and A. Kourtellos (2010) Should macroeconomic forecasters use daily financial data and how?, Discussion Paper UNC.

Andreou, E, Eric Ghysels and A. Kourtellos (2010) Forecasting with mixed-frequency data, Chapter prepared for Oxford Handbook on Economic Forecasting edited by Michael P. Clements and David F. Hendry

Chen, Xilong, Eric Ghysels and Fangfang Wang, (2010), The HYBRID GARCH Class of Models, Discussion Paper UNC.

Bai, J., Eric Ghysels and Jonathan Wright (2010), State Space Models and MIDAS Regressions, Discussion Paper UNC.

Eric Ghysels and J. Wright (2009), Forecasting Professional Forecasters, Journal of Business and Economic Statistics

Anderson, E., Eric Ghysels and J. Juergens (2009) The Impact of Risk and Uncertainty on Expected Returns, Journal of Financial Economics Eric Ghysels and B. Sohn (2009) Which Power Variation Predicts Volatility Well? Journal of Empirical Finance,

Andreou, E, Eric Ghysels and A. Kourtellos (2010) "Regression Models With Mixed Sampling Frequencies", Journal of Econometrics (Article in Press)

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

Riccardo Colacito, Robert Engle and Eric Ghysels A Component Model of Dynamic Correlations , Journal of Econometrics (Article in Press)

External links