Feasible invertibility conditions and maximum likelihood estimation for observation-driven models

DOI10.1214/18-EJS1416zbMath1473.62301arXiv1610.02863OpenAlexW2529383781MaRDI QIDQ1746551

Olivier Wintenberger, Siem Jan Koopman, Paolo Gorgi, Francisco Blasques

Publication date: 25 April 2018

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

Full work available at URL: https://arxiv.org/abs/1610.02863

zbMATH Keywords

consistencymaximum likelihood estimationinvertibilitystochastic recurrence equationsobservation-driven models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (13)

Semiparametric modeling of multiple quantiles ⋮ Missing observations in observation-driven time series models ⋮ A new time-varying model for forecasting long-memory series ⋮ Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects ⋮ Score-Driven Modeling of Spatio-Temporal Data ⋮ A robust score-driven filter for multivariate time series ⋮ Maximum likelihood estimation for non-stationary location models with mixture of normal distributions ⋮ Autoregressive conditional betas ⋮ Two classes of dynamic binomial integer-valued ARCH models ⋮ Clustering of arrivals in queueing systems: autoregressive conditional duration approach ⋮ Commercial and residential mortgage defaults: spatial dependence with frailty ⋮ A time-varying parameter model for local explosions ⋮ Maximum likelihood estimation for score-driven models

Uses Software

• CAViaR

Cites Work

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• Stationarity and ergodicity of univariate generalized autoregressive score processes
• Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: a stochastic recurrence equations approach
• Misspecified models with dependent observations
• On the invertibility of time series models
• GARCH processes: structure and estimation
• Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes
• Generalized autoregressive conditional heteroscedasticity
• Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models
• Estimation in conditionally heteroscedatic time series models.
• Pseudo-maximum likelihood estimation of \(\text{ARCH}(\infty)\) models
• On the measurability and consistency of minimum contrast estimates
• Observation-driven models for Poisson counts
• Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model
• Dynamic Models for Volatility and Heavy Tails
• Kalman Filtering with Random Coefficients and Contractions
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• MIXING PROPERTIES OF A GENERAL CLASS OF GARCH(1,1) MODELS WITHOUT MOMENT ASSUMPTIONS ON THE OBSERVED PROCESS
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• Using Least Squares to Approximate Unknown Regression Functions
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
• Consistency and Asymptotic Normality of the Quasi-Maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models
• Filtering With Heavy Tails
• ASYMPTOTIC INFERENCE FOR NONSTATIONARY GARCH
• Relations between Weak and Uniform Convergence of Measures with Applications
• Note on the Consistency of the Maximum Likelihood Estimate
• Maximum Likelihood Estimation of Misspecified Models

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