Asymptotic normality of error density estimator in stationary and explosive autoregressive models
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Publication:6542586
DOI10.1007/S11766-024-4558-XMaRDI QIDQ6542586
Min Gao, Wenzhi Yang, Shipeng Wu, Shuhe Hu
Publication date: 22 May 2024
Published in: Applied Mathematics. Series B (English Edition) (Search for Journal in Brave)
asymptotic distributionassociation sequenceexplosive autoregressive modelsresidual density estimator
Cites Work
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- Limit theory for moderate deviations from a unit root
- The Berry-Esseen bounds for kernel density estimator under dependent sample
- Limit theory for an explosive autoregressive process
- Mildly explosive autoregression under weak and strong dependence
- Global property of error density estimation in nonlinear autoregressive time series models
- Mildly explosive autoregression with mixing innovations
- Empirical likelihood for probability density functions under negatively associated samples
- Strong consistency of the distribution estimator in the nonlinear autoregressive time series
- Weak convergence of the residual empirical process in explosive autoregression
- A non-stationary integer-valued autoregressive model
- A general result on precise asymptotics for linear processes of positively associated sequences
- On smoothed probability density estimation for stationary processes
- An invariance principle for certain dependent sequences
- Time series: theory and methods.
- Asymptotics of the \(L_p\)-norms of density estimators in the first-order autoregressive models.
- Estimating the density of the residuals in autoregressive models
- A comparison theorem on moment inequalities between negatively associated and independent random variables
- Asymptotic normality of the kernel estimate of a probability density function under association
- On the Bickel-Rosenblatt test for first-order autoregressive models
- Glivenko-Cantelli theorem for the kernel error distribution estimator in the first-order autoregressive model
- Two step estimations for a single-index varying-coefficient model with longitudinal data
- Negative association of random variables, with applications
- Nonlinear time series. Nonparametric and parametric methods
- Empirical likelihood for first-order mixed integer-valued autoregressive model
- Asymptotic inference for \(\mathrm{AR}(1)\) panel data
- On some global measures of the deviations of density function estimates
- Parameter estimation for binomial \(\mathrm{AR}(1)\) models with applications in finance and industry
- A goodness-of-fit test of the errors in nonlinear autoregressive time series models
- Asymptotic normality of residual density estimator in stationary and explosive autoregressive models
- A limit theorem for mildly explosive autoregression with stable errors
- A goodness-of-fit test of the errors in nonlinear autoregressive time series models with stationary $\alpha$-mixing error terms
- Asymptotics for Associated Random Variables
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Weak consistency for the estimators in a semiparametric regression model based on negatively associated random errors
- Recursive probability density estimation for weakly dependent stationary processes
- Threshold Autoregression with a Unit Root
- Association of Random Variables, with Applications
- Analytic Inequalities
- Asymptotic normality of kernel density estimators under dependence
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