Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials

By Claudia Foroni, Massimiliano Marcellino, Christian Schumacher. 2015

263 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1c1d509849ddd782e669b6ba751fefe9f93ed6ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Eric Ghysels
2. Fulvio Corsi
3. Massimiliano Marcellino
4. Tim Bollerslev
5. Claudia Foroni
6. Torben G. Andersen
7. Jeannette H. C. Woerner
8. N. Shephard

In Finance

1. Eric Ghysels
2. Tim Bollerslev
3. Fulvio Corsi
4. Torben G. Andersen
5. Massimiliano Marcellino
6. Claudia Foroni
7. Robert F. Engle
8. N. Shephard

Citations in the corpus

• A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “Hence, our choice of the prior mean vector b reflects the view that the best prediction model is 9 Under the U-MIDAS approach of Foroni et al. (2013), the matrix of transformed regressors, X (m) t , is the same as the original matrix, X (m) t . a random walk.”
• Testing for Granger Causality with Mixed Frequency Data (2016, unknown): “A number of papers have linked MIDAS regressions to (latent) high frequency VAR models, such as Kuzin et al. (2011) and Foroni et al. (2015), whereas Ghysels (forthcoming) discusses the link between MF-VAR models and MIDAS regressions.”
• Can the COVID-19 Pandemic and Oil Prices Drive the US Partisan Conflict Index? (2020, unknown): “Such an unconstrainted MIDAS modelling is substantially useful when the number of high-frequency data linked to low-frequency observations) is small (Foroni et al., 2015).”
• Risk Everywhere: Modeling and Managing Volatility (2018, unknown): “2007, Corsi 2009), while the HAR-free model discussed in the footnote above is closely related to the so-called “U-MIDAS model” (Foroni et al. 2015).”
• Macroeconomics and the Reality of Mixed Frequency Data (2016, methodology): “One can view the completely unrestricted specification where each weight is estimated separately as inspired by the U-MIDAS approach suggested by Foroni et al. (2015) shown to work for small values ofm.”
• Testing for Granger Causality in Large Mixed-Frequency VARs (2016, unknown): “The top half of the table contains the outcomes for the first power DGP ( w ∗ j − 1 ( ψ ) and γ ∗ j, 1 , j = 2 , . . . , 21 , possess non-zero means), while the bottom half does so for the second power DGP ( w ∗ j − 1 ( ψ ) and γ ∗ j, 1 , j = 2 , . . . , 21 , possess means of zero).”