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FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure. (2011). Monteforte, Libero ; Frale, Cecilia .
In: Temi di discussione (Economic working papers).
RePEc:bdi:wptemi:td_788_11.

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  1. Nowcasting Key Australian Macroeconomic Variables. (2023). Anthonisz, Michael.
    In: Australian Economic Review.
    RePEc:bla:ausecr:v:56:y:2023:i:3:p:371-380.

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  2. Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences. (2019). Ferrara, Laurent ; Marsilli, Clement ; Banulescu-Radu, Denisa.
    In: LEO Working Papers / DR LEO.
    RePEc:leo:wpaper:2710.

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  3. Forecasting economic time series using score-driven dynamic models with mixed-data sampling. (2019). Li, Mengheng ; Koopman, Siem Jan ; Gorgi, Paolo.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:35:y:2019:i:4:p:1735-1747.

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  4. Forecasting economic time series using score-driven dynamic models with mixed-data sampling. (2018). Li, Mengheng ; Koopman, Siem Jan ; Gorgi, Paolo.
    In: Tinbergen Institute Discussion Papers.
    RePEc:tin:wpaper:20180026.

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  5. Forecasting Chinas GDP growth using dynamic factors and mixed-frequency data. (2017). Jiang, YU ; Zhang, Yihao ; Guo, Yongji .
    In: Economic Modelling.
    RePEc:eee:ecmode:v:66:y:2017:i:c:p:132-138.

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  6. Do We Need High Frequency Data to Forecast Variances?. (2016). Laurent, Sébastien ; Hurlin, Christophe ; Candelon, Bertrand ; BANULESCU-RADU, Denisa.
    In: Post-Print.
    RePEc:hal:journl:hal-01448237.

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  7. Do We Need Ultra-High Frequency Data to Forecast Variances?. (2014). Laurent, Sébastien ; Hurlin, Christophe ; Candelon, Bertrand ; BANULESCU-RADU, Denisa.
    In: Working Papers.
    RePEc:hal:wpaper:halshs-01078158.

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References

References cited by this document

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