Estimating MIDAS regressions via OLS with polynomial parameter profiling

By Eric Ghysels, Hang Qian. Econometrics and Statistics, 2019

68 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/352dbc6b4d45d4ea28531281b80c786d20ce64d8

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In Analytical Chemistry

1. Zhi Su
2. Tae-Hwy Lee
3. Tong Fang

In Statistics and Probability

1. Zhi Su
2. Tae-Hwy Lee
3. Tong Fang

Citations in the corpus

• Risk Everywhere: Modeling and Managing Volatility (2018, unknown): “Lastly, following the approach of Ghysels and Qian (2016), we determine the remaining θ2 tuning parameter by a grid search, in which we profile the predictive R2 from the model as a function of θ2, together with the freely estimated β0 and β1 parameters, and choose the value of θ2 that maximizes the”
• Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, methodology): “We also use an estimate procedure which is similar to the parameter profiling in Ghysels and Qian (2019), in order to solve the identification issues and provide a computationally attractive estimation procedure.”