Subsampling for heteroskedastic time series

DOI10.1016/S0304-4076(97)86569-4zbMath0904.62059OpenAlexW2059047279WikidataQ60962280 ScholiaQ60962280MaRDI QIDQ1372916

Joseph P. Romano, Michael Wolf, Dimitris N. Politis

Publication date: 4 November 1997

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

Full work available at URL: https://doi.org/10.1016/s0304-4076(97)86569-4

zbMATH Keywords

time seriessubsamplingheteroskedasticitymoving blocks bootstrapcentral limit theorem for triangular arrays

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Nonparametric tolerance and confidence regions (62G15) Nonparametric statistical resampling methods (62G09)

Related Items

Maximum likelihood and the bootstrap for nonlinear dynamic models, Bootstrapping the Box-Pierce \(Q\) test: a robust test of uncorrelatedness, Large sample theory for statistics of stable moving averages, The moving blocks bootstrap and robust inference for linear least squares and quantile regressions, Wild bootstrapping variance ratio tests, A WILD BOOTSTRAP FOR DEPENDENT DATA, An alternative bootstrap to moving blocks for time series regression models, A general central limit theorem for strong mixing sequences, Comparison of stationary time series using distribution-free methods, Subsampling high frequency data, Comment on: Subsampling weakly dependent time series and application to extremes, Properties of the nonparametric autoregressive bootstrap, Inference without smoothing for large panels with cross-sectional and temporal dependence, EMPIRICAL LIKELIHOOD CONFIDENCE INTERVALS FOR DEPENDENT DURATION DATA, Theoretical Measures of Relative Performance of Classifiers for High Dimensional Data with Small Sample Sizes, Valid hypothesis testing in face of spatially dependent data using multi-layer perceptrons and sub-sampling techniques, Detecting business cycle asymmetries using artificial neural networks and time series models, Improved nonparametric confidence intervals in time series regressions, Tests of random walk: A comparison of bootstrap approaches, Exact asymptotics for estimating the marginal density of discretely observed diffusion proc\-esses, Finite-sample Resampling-based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability, Range Unit-Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers, Quenched law of large numbers and quenched central limit theorem for multiplayer leagues with ergodic strengths

Uses Software

• bootstrap

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Pricing of Options and Corporate Liabilities
• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• On bootstrapping two-stage least-squares estimates in stationary linear models
• Bootstrap methods in statistics
• The use of subseries values for estimating the variance of a general statistic from a stationary sequence
• Edgeworth correction by bootstrap in autoregressions
• Bootstrap procedures under some non-i.i.d. models
• Bootstrapping regression models
• On the asymptotic accuracy of Efron's bootstrap
• On bootstrapping kernel spectral estimates
• A general resampling scheme for triangular arrays of \(\alpha\)-mixing random variables with application to the problem of spectral density estimation
• Bootstrap methods: another look at the jackknife
• Mixing: Properties and examples
• Blockwise bootstrapped empirical process for stationary sequences
• The moving blocks bootstrap and robust inference for linear least squares and quantile regressions
• Block length selection in the bootstrap for time series
• Jackknife, bootstrap and other resampling methods in regression analysis
• The jackknife and the bootstrap for general stationary observations
• Large sample confidence regions based on subsamples under minimal assumptions
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• Some Limit Theorems for Random Functions. I
• Nonlinear Regression with Dependent Observations
• Calibrating Confidence Coefficients
• An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
• The Stationary Bootstrap
• On blocking rules for the bootstrap with dependent data
• Contributions to Central Limit Theory for Dependent Variables
• The Invariance Principle for Stationary Processes
• Functional central limit theorems for strictly stationary processes satisfying the strong mixing condition
• Some Limit Theorems for Stationary Processes
• The bootstrap and Edgeworth expansion