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Estimating MIDAS regressions via OLS with polynomial parameter profiling

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  • Ghysels, Eric
  • Qian, Hang

Abstract

A typical MIDAS regression involves estimating parameters via nonlinear least squares, unless U-MIDAS is applied – which involves OLS – the latter being appealing when the sampling frequency differences are small. It is proposed to use OLS estimation of the MIDAS regression slope and intercept parameters combined with profiling the polynomial weighting scheme parameter(s). The use of Beta polynomials is particularly attractive for such an approach. The new procedure shares many of the desirable features of U-MIDAS, while it is not restricted to small sampling frequency differences.

Suggested Citation

  • Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    • Handle: RePEc:eee:ecosta:v:9:y:2019:i:c:p:1-16
    DOI: 10.1016/j.ecosta.2018.02.001
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    References listed on IDEAS

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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