SEMIFAR models -- a semiparametric approach to modelling trends, long-range dependence and nonstationarity
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Publication:1608913
DOI10.1016/S0167-9473(02)00007-5zbMath0993.62079OpenAlexW2081004852MaRDI QIDQ1608913
Publication date: 13 August 2002
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00007-5
kernel estimationsemiparametric modelslong-range dependencebandwidth selectiontrendsBICdifferencinganti-persistence
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
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Uses Software
Cites Work
- Nonparametric regression with long-range dependence
- Efficient parameter estimation for self-similar processes
- On the quadratic mapping \(z\rightarrow z^{2}-\mu \) for complex \(\mu \) and \(z\): the fractal structure of its set, and scaling
- Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series
- Bandwidth selection for kernel estimate with correlated noise
- Estimating the dimension of a model
- Nonparametric regression under long-range dependent normal errors
- A central limit theorem for quadratic forms in strongly dependent linear variables and its application to asymptotical normality of Whittle's estimate
- A Flexible and Fast Method for Automatic Smoothing
- ON ESTIMATION OF LONG-MEMORY TIME SERIES MODELS
- Fractional differencing
- AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
- Choice of bandwidth for kernel regression when residuals are correlated
- A Bayesian extension of the minimum AIC procedure of autoregressive model fitting
- Linear Trend with Fractionally Integrated Errors
- On unified model selection for stationary and nonstationary short- and long-memory autoregressive processes
- Locally Adaptive Bandwidth Choice for Kernel Regression Estimators
- On Fractionally Integrated Autoregressive Moving-Average Time Series Models With Conditional Heteroscedasticity
- Arbitrage with Fractional Brownian Motion
- Bandwidth selection for kernel regression with long-range dependent errors
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