Long-term prediction intervals of economic time series
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Cited by:
- Kejin Wu & Sayar Karmakar & Rangan Gupta, 2023.
"GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables,"
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2308.13346, arXiv.org, revised Sep 2024.
- Kejin Wu & Sayar Karmakar & Rangan Gupta, 2024. "GARCHX-NoVaS: A Model-Free Approach to Incorporate Exogenous Variables," Working Papers 202425, University of Pretoria, Department of Economics.
- Demirel, Ufuk Devrim & Otterson, James, 2023. "Quantifying the uncertainty of long-term macroeconomic projections," Journal of Macroeconomics, Elsevier, vol. 75(C).
- Kejin Wu & Sayar Karmakar, 2023. "A model-free approach to do long-term volatility forecasting and its variants," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
- Kejin Wu & Sayar Karmakar & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa," Working Papers 202326, University of Pretoria, Department of Economics.
- Kejin Wu & Sayar Karmakar, 2021. "Model-Free Time-Aggregated Predictions for Econometric Datasets," Forecasting, MDPI, vol. 3(4), pages 1-14, December.
- David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
- Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020. "Long-term prediction intervals with many covariates," Papers 2012.08223, arXiv.org, revised Sep 2021.
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This paper has been announced in the following NEP Reports:- NEP-ETS-2020-03-02 (Econometric Time Series)
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