Asymptotic theory for time series with changing mean and variance

DOI10.1016/j.jeconom.2020.03.005zbMath1464.62378OpenAlexW3014064947MaRDI QIDQ2224882

Peter M. Robinson, Violetta Dalla, Liudas Giraitis

Publication date: 4 February 2021

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://qmro.qmul.ac.uk/xmlui/handle/123456789/60672

zbMATH Keywords

nonparametric heteroscedasticitynonparametric moving meanparametric autocorrelationsemiparametric time series model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

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Cites Work

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• Adaptive estimation of autoregressive models with time-varying variances
• Gaussian pseudo-maximum likelihood estimation of fractional time series models
• Nonparametric curve estimation with time series errors
• Nonparametric regression with heteroscedastic long memory errors
• Nonparametric estimation of volatility models with serially dependent innovations
• Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series
• Estimating error correlation in nonparametric regression
• Fitting time series models to nonstationary processes
• Large-sample inference for nonparametric regression with dependent errors
• SEMIFAR models -- a semiparametric approach to modelling trends, long-range dependence and nonstationarity
• Gaussian estimation of parametric spectral density with unknown pole
• A nonlinear long memory model, with an application to US unemployment.
• Quasi-maximum likelihood estimation and bootstrap inference in fractional time series models with heteroskedasticity of unknown form
• Autoregressive coefficient estimation in nonparametric analysis
• Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends
• Nonparametric regression with rescaled time series errors
• NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE
• Local Whittle estimation of the memory parameter in presence of deterministic components
• Inference in Autoregression under Heteroskedasticity
• Discrimination between monotonic trends and long-range dependence
• Forecasting the U.S. Unemployment Rate
• Estimating and Testing Linear Models with Multiple Structural Changes
• Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series
• Using Difference-Based Methods for Inference in Nonparametric Regression with Time Series Errors
• EFFICIENT ESTIMATION OF NONSTATIONARY TIME SERIES REGRESSION
• MEMORY PARAMETER ESTIMATION IN THE PRESENCE OF LEVEL SHIFTS AND DETERMINISTIC TRENDS
• On the Asymptotic Distribution of the Autocorrelations of a Sample from a Linear Stochastic Process
• Martingale Central Limit Theorems
• The asymptotic theory of linear time-series models
• Estimation of Long Memory in the Presence of a Smooth Nonparametric Trend