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This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions; where the copulas capture temporal dependence...
Persistent link: https://www.econbiz.de/10005762792
We consider nonparametric estimation of spectral densities of stationary processes, a fundamental problem in spectral analysis of time series. Under natural and easily verifiable conditions, we obtain consistency and asymptotic normality of spectral density estimates. Asymptotic distribution of...
Persistent link: https://www.econbiz.de/10008520680
We consider nonparametric prediction problem for both short- and long-range-dependent linear processes. Asymptotic properties of local linear estimates are obtained and, for long-range-dependent processes, an interesting dichotomous phenomenon is described: the limiting distribution depends on...
Persistent link: https://www.econbiz.de/10008536917
We study nonparametric inference of stochastic models driven by stable Lévy processes. We introduce a nonparametric estimator of the stable index that achieves the parametric rate of convergence. For the volatility function, due to the heavy-tailedness, the classical least-squares method is not...
Persistent link: https://www.econbiz.de/10008493171
Persistent link: https://www.econbiz.de/10006955266
We consider statistical inference of trends in mean non-stationary models. A test statistic is proposed for the existence of structural breaks in trends. On the basis of a strong invariance principle of stationary processes, we construct simultaneous confidence bands with asymptotically correct...
Persistent link: https://www.econbiz.de/10005140175
The paper considers kernel estimation of conditional quantiles for both short-range and long-range-dependent processes. Under mild regularity conditions, we obtain Bahadur representations and central limit theorems for kernel quantile estimates of those processes. Our theory is applicable to...
Persistent link: https://www.econbiz.de/10004998208
<p>This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant distributions and parametric copula functions; where the copulas capture all scale-free temporal dependence and tail dependence of...</p>
Persistent link: https://www.econbiz.de/10005037577
Persistent link: https://www.econbiz.de/10005411766
Persistent link: https://www.econbiz.de/10005610316