Temporally Local Maximum Likelihood with Application to SIS Model

Gourieroux Christian () and Joann Jasiak
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Gourieroux Christian: University of Toronto, Toronto, Canada

Journal of Time Series Econometrics, 2023, vol. 15, issue 2, 151-198

Abstract: 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.

Keywords: local maximum likelihood; rolling estimator; omitted heterogeneity; bias reduction; generalized linear model; SIS model; logistic growth; ultra long run (search for similar items in EconPapers)
JEL-codes: C01 C13 C22 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jtsmet:v:15:y:2023:i:2:p:151-198:n:3

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DOI: 10.1515/jtse-2022-0016

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