Time varying Markov process with partially observed aggregate data: An application to coronavirus
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DOI: 10.1016/j.jeconom.2020.09.007
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- Christian GOURIEROUX & Joann JASIAK, 2020. "Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus," Working Papers 2020-11, Center for Research in Economics and Statistics, revised 08 May 2020.
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- Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.
- Mahapatra, D.P. & Triambak, S., 2022. "Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
- Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone & Maygol Bandehali, 2022. "Transition model for coronavirus management," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 665-704, February.
- Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.
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Keywords
Markov process; Partial observability; Information recovery; Estimating equations; SIR model; Coronavirus; Infection rate;All these keywords.
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