Ergodicity of nonlinear first order autoregressive models
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Publication:1345087
DOI10.1007/BF02213462zbMath0815.60059MaRDI QIDQ1345087
Chanho Lee, Rabi N. Bhattacharya
Publication date: 26 February 1995
Published in: Journal of Theoretical Probability (Search for Journal in Brave)
Strong limit theorems (60F15) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)
Related Items (16)
Stability of nonlinear AR(1) time series with delay ⋮ Drift conditions and invariant measures for Markov chains. ⋮ Geometric ergodicity of nonlinear autoregressive models with changing conditional variances ⋮ Limit theorems for some doubly stochastic processes ⋮ Efficient prediction for linear and nonlinear autoregressive models ⋮ Distribution-free strong consistency for nonparametric kernel regression involving nonlinear time series ⋮ Existence of bounded invariant probability densities for Markov chains ⋮ Nonparametric semirecursive identification in a wide sense of strong mixing processes ⋮ Causality in extremes of time series ⋮ Testing for changes in the mean or variance of a stochastic process under weak invariance ⋮ Ergodicity and existence of moments for local mixtures of linear autoregressions ⋮ \(L_1\) geometric ergodicity of a multivariate nonlinear AR model with an ARCH term. ⋮ Kernel estimates of the mean and the volatility functions in a nonlinear autoregressive model with ARCH errors ⋮ Estimating the innovation distribution in nonparametric autoregression ⋮ Stationary bootstrapping for non-parametric estimator of nonlinear autoregressive model ⋮ Conditionally minimax nonlinear filter and unscented Kalman filter: empirical analysis and comparison
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