Two-stage generalized moment method approach for bidimensional random coefficient autoregressive models

Bibi, Abdelouahab

doi:10.1080/03610926.2014.919401

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Random Coefficient Autoregressive Models (Rca)1

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2020

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Cited by 3 publications

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“…Under suitable conditions, they derive strong consistency and asymptotic normality for these estimates. Recently the generalized moment estimator and the Whittle estimator (involving an approximation to the likelihood function) have also been proposed (Shitan et al, 2015;Bibi, 2016).…”

Section: Random Coefficient Autoregressive Models (Rca)mentioning

confidence: 99%

Random autoregressive models: A structured overview

Regis¹,

Serra²,

Heuvel³

2020

Preprint

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Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The available literature on such models is broad, but also sectorial, overlapping, and confusing. Most models focus on one property of the data, while much can be gained by combining the strength of various models and their sources of heterogeneity.We present a structured overview of the literature on autoregressive models with random coefficients. We describe hierarchy and analogies among models, and for each we systematically list properties, estimation methods, tests, software packages and typical applications.

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Section: Random Coefficient Autoregressive Models (Rca)mentioning

confidence: 99%