Testing the functions defining a nonlinear autoregressive time series

DOI10.1016/0304-4149(90)90044-SzbMath0704.62083MaRDI QIDQ917203

Jean Diebolt

Publication date: 1990

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

zbMATH Keywords

consistencyWiener processconvergence in distributiongoodness-of-fit testsnumerical simulationfunctional limit theoremphi-mixingnonlinear autoregressive time seriesregressogram type estimatorsstationary random sequence

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional limit theorems; invariance principles (60F17)

Related Items (10)

Joint parametric specification checking of conditional mean and volatility in time series models with martingale difference innovations ⋮ A nonparametric test for the regression function: Asymptotic theory ⋮ Weak convergence of some marked empirical processes: Application to testing heteroscedasticity ⋮ Estimates of the tail of the stationary density function of certain nonlinear autoregressive processes ⋮ Kernel estimates of the mean and the volatility functions in a nonlinear autoregressive model with ARCH errors ⋮ Nonparametric testing for correlation models with dependent data ⋮ Approximations for hybrids of empirical and partial sums processes ⋮ A nonparametric goodness-of-fit test for a class of parametric autoregressive models ⋮ A weak invariance principle for cumulated functionals of the regressogram estimator with dependent data ⋮ On Complete Convergence for the Hybrid Process

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