Semiparametric Lower Bounds for Tail Index Estimation

indexation;semiparametric estimation

The asymptotic structure of nearly unstable non-negative integer-valued AR(1) models

This paper considers non-negative integer-valued autoregressive processes where the autoregression parameter is close to unity. We consider the asymptotics of this `near unit root' situation. The local asymptotic structure of the likelihood ratios of the model is obtained, showing that the limit experiment is Poissonian. To illustrate the statistical consequences we discuss efficient estimation of the autoregression parameter and efficient testing for a unit root.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ153 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Semiparametric lower bounds for tail-index estimation

info:eu-repo/semantics/publishe

Efficient Estimation in Semiparametric Time Series: the ACD Model

In this paper we consider efficient estimation in semiparametric ACD models. We consider a suite of model specifications that impose less and less structure. We calculate the corresponding efficiency bounds, discuss the construction of efficient estimators in each case, and study tvide a simulation study that shows the practical gain from using the proposed semiparametric procedures. We find that, although one does not gain as much as theory suggests, these semiparametric procedures definitely outperform more classical procedures. We apply the procedures to model semiparametrically durations observed on the Paris Bourse for the Alcatel stock in July and August 1996.

Semiparametrically Point-Optimal Hybrid Rank Tests for Unit Roots

We propose a new class of unit root tests that exploits invariance properties in the Locally Asymptotically Brownian Functional limit experiment associated to the unit root model. The invariance structures naturally suggest tests that are based on the ranks of the increments of the observations, their average, and an assumed reference density for the innovations. The tests are semiparametric in the sense that they are valid, i.e., have the correct (asymptotic) size, irrespective of the true innovation density. For a correctly specified reference density, our test is point-optimal and nearly efficient. For arbitrary reference densities, we establish a Chernoff-Savage type result, i.e., our test performs as well as commonly used tests under Gaussian innovations but has improved power under other, e.g., fat-tailed or skewed, innovation distributions. To avoid nonparametric estimation, we propose a simplified version of our test that exhibits the same asymptotic properties, except for the Chernoff-Savage result that we are only able to demonstrate by means of simulations

Producing holograms of reacting sprays in liquid propellant rocket engines Final report

Holograms and laser-illuminated photography of reacting sprays in liquid propellant rocket engine