A Minimum Distance Lack-of-Fit Test in a Markovian Multiplicative Error Model

Koul, Hira L.; Perera, Indeewara

doi:10.1007/s42519-021-00168-1

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2021

2022

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References 27 publications

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“…These models have been found to be useful for modeling a variety of non-negative time series. Numerous applications and properties of these models are discussed in Engle and Russell (1998), Bauwens and Giot (2000), Bauwens and Veredas (2004), Manganelli (2005), Chou (2005), Engle and Gallo (2006), Fernandes and Grammig (2006), Gao et al (2015) and Koul and Perera (2021), among others.…”

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confidence: 99%

A class of Minimum Distance Estimators in Markovian Multiplicative Error Models

2022

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This paper proposes a class of minimum distance estimators for the underlying parameters in a Markovian parametric multiplicative error time series model. This class of estimators is based on the integrals of the square of a certain marked residual process. The paper derives the asymptotic distributions of the proposed estimators. In a finite sample comparison, some members of the proposed class of estimators dominate a generalized method of moments estimator in terms of the finite sample bias at a variety of chosen error distributions while neither dominate each other in terms of the mean squared error at these error distributions. A real data example is considered to illustrate the proposed estimation procedures.

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