Residual Empirical Processes and Weighted Sums for Time-Varying Processes with Applications to Testing for Homoscedasticity
DOI10.1111/jtsa.12200zbMath1356.62123OpenAlexW2419146988MaRDI QIDQ2954305
Wolfgang Polonik, Gabe Chandler
Publication date: 12 January 2017
Published in: Journal of Time Series Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/jtsa.12200
cumulantsexponential inequalitynon-stationary processeslocally stationary processesempirical process theorytests of stationarity
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Non-Markovian processes: hypothesis testing (62M07)
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Cites Work
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- Nonparametric estimation of time varying parameters under shape restrictions
- Empirical spectral processes for locally stationary time series
- Testing temporal constancy of the spectral structure of a time series
- Estimating the error distribution function in semiparametric additive regression models
- Asymptotic distributions of error density and distribution function estimators in nonparametric regression
- Estimating the error distribution in nonparametric multiple regression with applications to model testing
- Estimating the error distribution function in nonparametric regression with multivariate co\-var\-iates
- Estimating the innovation distribution in nonparametric autoregression
- Goodness-of-fit tests in parametric regression based on the estimation of the error distribution
- A uniform central limit theorem for set-indexed partial-sum processes with finite variance
- On tail probabilities for martingales
- An efficient estimator for the expectation of a bounded function under the residual distribution of an autoregressive process
- Fitting time series models to nonstationary processes
- Weighted empirical processes in dynamic nonlinear models.
- Empirical process of the squared residuals of an ARCH sequence
- Estimating the innovation distribution in nonlinear autoregressive models
- Residual analysis for \(\text{ARCH}(p)\)-time series.
- Locally adaptive fitting of semiparametric models to nonstationary time series.
- Analysis of acoustic signatures from moving vehicles using time-varying autoregressive models
- Nonlinear wavelet estimation of time-varying autoregressive processes
- Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes
- A test for stationarity based on empirical processes
- Fitting an error distribution in some heteroscedastic time series models
- Bracketing metric entropy rates and empirical central limit theorems for function classes of Besov- and Sobolev-type
- A Wavelet-Based Test for Stationarity
- Non-parametric Estimation of the Residual Distribution
- Estimation of the Distribution of Noise in an Autoregression Scheme
- A Haar–Fisz technique for locally stationary volatility estimation
- Testing for Stationarity in Multivariate Locally Stationary Processes
- On residual empirical processes of GARCH-SM models: application to conditional symmetry tests
- Mode Identification of Volatility in Time-Varying Autoregression
- Validating Stationarity Assumptions in Time Series Analysis by Rolling Local Periodograms
- Estimating the error distribution function in semiparametric regression
- Discrimination of Locally Stationary Time Series Based on the Excess Mass Functional
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