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Variable Selection in Predictive MIDAS Models. (2014). Marsilli, Clément.
In: Working papers.
RePEc:bfr:banfra:520.

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  1. Machine learning panel data regressions with heavy-tailed dependent data: Theory and application. (2023). Babii, Andrii ; Ghysels, Eric ; Ball, Ryan T ; Striaukas, Jonas.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:237:y:2023:i:2:s0304407622001282.

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  2. Nowcasting world GDP growth with high?frequency data. (2022). Meunier, Baptiste ; Jardet, Caroline.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:41:y:2022:i:6:p:1181-1200.

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  3. Forecasting the Japanese macroeconomy using high-dimensional data. (2022). Sueishi, Naoya ; Nakajima, Yoshiki.
    In: The Japanese Economic Review.
    RePEc:spr:jecrev:v:73:y:2022:i:2:d:10.1007_s42973-020-00041-z.

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  4. Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model. (2022). Yang, Chenxu ; Xuyang, Chen ; Chuang, O-Chia .
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:8:p:2945-:d:795842.

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  5. Forecasting crude oil volatility with uncertainty indicators: New evidence. (2022). Umar, Muhammad ; Chen, Zhonglu ; Liang, Chao.
    In: Energy Economics.
    RePEc:eee:eneeco:v:108:y:2022:i:c:s0140988322001141.

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  6. Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty. (2021). Xiong, Wei ; Jiang, Cuixia ; Liu, Yezheng ; Xu, Qifa.
    In: Finance Research Letters.
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  7. Bayesian MIDAS penalized regressions: Estimation, selection, and prediction. (2021). Mogliani, Matteo ; Simoni, Anna.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:222:y:2021:i:1:p:833-860.

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  8. Machine Learning Time Series Regressions With an Application to Nowcasting. (2021). Striaukas, Jonas ; Ghysels, Eric ; Babii, Andrii.
    In: LIDAM Discussion Papers LFIN.
    RePEc:ajf:louvlf:2021004.

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  9. Machine learning time series regressions with an application to nowcasting. (2020). Babii, Andrii ; Striaukas, Jonas ; Ghysels, Eric.
    In: Papers.
    RePEc:arx:papers:2005.14057.

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  10. Estimating MIDAS regressions via OLS with polynomial parameter profiling. (2019). Ghysels, Eric ; Qian, Hang.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:9:y:2019:i:c:p:1-16.

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  11. Bayesian MIDAS penalized regressions: estimation, selection, and prediction. (2019). Mogliani, Matteo.
    In: Working papers.
    RePEc:bfr:banfra:713.

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  12. Estimation and HAC-based Inference for Machine Learning Time Series Regressions. (2019). Striaukas, Jonas ; Ghysels, Eric ; Babii, Andrii.
    In: Papers.
    RePEc:arx:papers:1912.06307.

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  13. Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction. (2019). Mogliani, Matteo.
    In: Papers.
    RePEc:arx:papers:1903.08025.

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  14. Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth. (2018). Xu, Qifa ; Liu, Yezheng ; Jiang, Cuixia ; Zhuo, Xingxuan.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:75:y:2018:i:c:p:221-236.

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  15. Testing for Granger causality in large mixed-frequency VARs. (2015). Smeekes, Stephan ; Hecq, Alain ; Götz, Thomas ; Gotz, Thomas B.
    In: Discussion Papers.
    RePEc:zbw:bubdps:452015.

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  16. Testing for Granger Causality in Large Mixed-Frequency VARs. (2015). Smeekes, Stephan ; Hecq, Alain ; Götz, Thomas.
    In: Research Memorandum.
    RePEc:unm:umagsb:2015036.

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  17. Short-term forecasting with mixed-frequency data: A MIDASSO approach. (2015). Siliverstovs, Boriss.
    In: KOF Working papers.
    RePEc:kof:wpskof:15-375.

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    References contributed by pko254-17898

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