Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models

DOI10.1214/17-EJS1299zbMath1366.62173OpenAlexW2731557133MaRDI QIDQ2362685

Konstantinos Fokianos, Eric Moulines, Randal Douc

Publication date: 11 July 2017

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1499133752

zbMATH Keywords

consistencyasymptotic normalitymaximum likelihoodstationarityKullback-Leibler divergenceduration modelsGARCH modelscount time series

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05) Discrete-time Markov processes on general state spaces (60J05)

Related Items (9)

Piecewise autoregression for general integer-valued time series ⋮ Observation-driven models for discrete-valued time series ⋮ Exponential family QMLE-based CUSUM test for integer-valued time series ⋮ Feasible invertibility conditions and maximum likelihood estimation for observation-driven models ⋮ Testing Linearity for Network Autoregressive Models ⋮ Multivariate count autoregression ⋮ Flexible bivariate Poisson integer-valued GARCH model ⋮ Mixtures of Nonlinear Poisson Autoregressions ⋮ Modeling and inference for multivariate time series of counts based on the INGARCH scheme

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