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Temporally Local Maximum Likelihood with Application to SIS Model

Christian Gouriéroux & Joann Jasiak

Journal of Time Series Econometrics2023https://doi.org/10.1515/jtse-2022-0016article
AJG 2ABDC B
Weight
0.26

What the paper says

The parametric estimators applied by rolling are commonly used for the analysis of time series with nonlinear patterns, including time varying parameters and local trends. This paper examines the properties of rolling estimators in the class of temporally local maximum likelihood (TLML) estimators. We consider the TLML estimators of (a) constant parameters, (b) stochastic, stationary parameters and (c) parameters with the ultra-long run (ULR) dynamics bridging the gap between the constant and stochastic parameters. We show that the weights used in the TLML estimators have a strong impact on the inference. For illustration, we provide a simulation study of the epidemiological susceptible-infected-susceptible (SIS) model, which explores the finite sample performance of TLML estimators of a time varying contagion parameter.

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https://doi.org/https://doi.org/10.1515/jtse-2022-0016

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@article{christian2023,
  title        = {{Temporally Local Maximum Likelihood with Application to SIS Model}},
  author       = {Christian Gouriéroux & Joann Jasiak},
  journal      = {Journal of Time Series Econometrics},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2022-0016},
}

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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