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Predictions from ARMAX models

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  • Baillie, Richard T.

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  • Baillie, Richard T., 1980. "Predictions from ARMAX models," Journal of Econometrics, Elsevier, vol. 12(3), pages 365-374, April.
    • Handle: RePEc:eee:econom:v:12:y:1980:i:3:p:365-374
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    Cited by:

    1. Miaomiao Niu & Guohao Li, 2022. "The Impact of Climate Change Risks on Residential Consumption in China: Evidence from ARMAX Modeling and Granger Causality Analysis," IJERPH, MDPI, vol. 19(19), pages 1-15, September.
    2. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
    3. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    4. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.
    5. Hlouskova, Jaroslava & Schmidheiny, Kurt & Wagner, Martin, 2009. "Multistep predictions for multivariate GARCH models: Closed form solution and the value for portfolio management," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 330-336, March.
    6. Edi Sukamto & Dadang Gunawan, 2016. "Dynamic detection system design of fraud simbox to improve quality service of international incoming call," Journal of Applied and Physical Sciences, Prof. Vakhrushev Alexander, vol. 2(3), pages 77-81.
    7. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.
    8. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    9. Abdelmonaem Jornaz & V. A. Samaranayake, 2019. "A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines," Energies, MDPI, vol. 12(21), pages 1-22, November.
    10. Rama K. Malladi & Prakash L. Dheeriya, 2021. "Time series analysis of Cryptocurrency returns and volatilities," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(1), pages 75-94, January.
    11. Llewellyn, Mary & Ross, Gordon & Ryan-Saha, Joshua, 2023. "COVID-era forecasting: Google trends and window and model averaging," Annals of Tourism Research, Elsevier, vol. 103(C).
    12. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.

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