Bootstrapping multivariate portmanteau tests for vector autoregressive models with weak assumptions on errors
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Publication:2242146
DOI10.1016/j.csda.2021.107321OpenAlexW3185365215WikidataQ108871220 ScholiaQ108871220MaRDI QIDQ2242146
Publication date: 9 November 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2021.107321
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- A bootstrapped spectral test for adequacy in weak ARMA models
- Multivariate portmanteau test for structural VARMA models with uncorrelated but non-independent error terms
- Testing for co-integration in vector autoregressions with non-stationary volatility
- A bootstrap-assisted spectral test of white noise under unknown dependence
- Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
- Bootstrapping general empirical measures
- Diagnostic checking of multivariate nonlinear time series models with martingale difference errors
- Computing the distribution of quadratic forms: further comparisons between the Liu-Tang-Zhang approximation and exact methods
- Consistent autoregressive spectral estimates
- Jackknife, bootstrap and other resampling methods in regression analysis
- The jackknife and the bootstrap for general stationary observations
- Least absolute deviation estimation for all-pass time series models
- Consistent specification tests for semiparametric/nonparametric models based on series estimation methods
- Strict stationarity testing and GLAD estimation of double autoregressive models
- Testing serial correlations in high-dimensional time series via extreme value theory
- A simple resampling method by perturbing the minimand
- Selection of weak VARMA models by modified Akaike's information criteria
- Inference For Autocorrelations Under Weak Assumptions
- The Multivariate Portmanteau Statistic
- On a measure of lack of fit in time series models
- Automatic Lag Selection in Covariance Matrix Estimation
- TESTING FOR ZERO AUTOCORRELATION IN THE PRESENCE OF STATISTICAL DEPENDENCE
- Consistent Testing for Serial Correlation of Unknown Form
- A HYBRID BOOTSTRAP APPROACH TO UNIT ROOT TESTS
- Quantile Correlations and Quantile Autoregressive Modeling
- Bootstrapping the Portmanteau Tests in Weak Auto-Regressive Moving Average Models
- Testing for high-dimensional white noise using maximum cross-correlations
- Multivariate Portmanteau Test For Autoregressive Models with Uncorrelated but Nonindependent Errors
- Convergence of Distributions Generated by Stationary Stochastic Processes
- Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
- Diagnostic Checking in ARMA Models With Uncorrelated Errors
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