1,152 research outputs found
Quasi maximum likelihood estimation for strongly mixing state space models and multivariate L\'evy-driven CARMA processes
We consider quasi maximum likelihood (QML) estimation for general
non-Gaussian discrete-ime linear state space models and equidistantly observed
multivariate L\'evy-driven continuoustime autoregressive moving average
(MCARMA) processes. In the discrete-time setting, we prove strong consistency
and asymptotic normality of the QML estimator under standard moment assumptions
and a strong-mixing condition on the output process of the state space model.
In the second part of the paper, we investigate probabilistic and analytical
properties of equidistantly sampled continuous-time state space models and
apply our results from the discrete-time setting to derive the asymptotic
properties of the QML estimator of discretely recorded MCARMA processes. Under
natural identifiability conditions, the estimators are again consistent and
asymptotically normally distributed for any sampling frequency. We also
demonstrate the practical applicability of our method through a simulation
study and a data example from econometrics
Rank-based optimal tests of the adequacy of an elliptic VARMA model
We are deriving optimal rank-based tests for the adequacy of a vector
autoregressive-moving average (VARMA) model with elliptically contoured
innovation density. These tests are based on the ranks of pseudo-Mahalanobis
distances and on normed residuals computed from Tyler's [Ann. Statist. 15
(1987) 234-251] scatter matrix; they generalize the univariate signed rank
procedures proposed by Hallin and Puri [J. Multivariate Anal. 39 (1991) 1-29].
Two types of optimality properties are considered, both in the local and
asymptotic sense, a la Le Cam: (a) (fixed-score procedures) local asymptotic
minimaxity at selected radial densities, and (b) (estimated-score procedures)
local asymptotic minimaxity uniform over a class F of radial densities.
Contrary to their classical counterparts, based on cross-covariance matrices,
these tests remain valid under arbitrary elliptically symmetric innovation
densities, including those with infinite variance and heavy-tails. We show that
the AREs of our fixed-score procedures, with respect to traditional (Gaussian)
methods, are the same as for the tests of randomness proposed in Hallin and
Paindaveine [Bernoulli 8 (2002b) 787-815]. The multivariate serial extensions
of the classical Chernoff-Savage and Hodges-Lehmann results obtained there thus
also hold here; in particular, the van der Waerden versions of our tests are
uniformly more powerful than those based on cross-covariances. As for our
estimated-score procedures, they are fully adaptive, hence, uniformly optimal
over the class of innovation densities satisfying the required technical
assumptions.Comment: Published at http://dx.doi.org/10.1214/009053604000000724 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Using Subspace Methods for Estimating ARMA Models for Multivariate Time Series with Conditionally Heteroskedastic Innovations
This paper deals with the estimation of linear dynamic models of the ARMA type for the conditional mean for time series with conditionally heteroskedastic innovation process widely used in modelling financial time series. Estimation is performed using subspace methods which are known to have computational advantages as compared to prediction error methods based on criterion minimization. These advantages are especially strong for high dimensional time series. The subspace methods are shown to provide consistent estimators. Moreover asymptotic equivalence to prediction error estimators in terms of the asymptotic variance is proved. Also order estimation techniques are proposed and analyzed. The estimators are not efficient as they do not model the conditional variance. Nevertheless, they can be used to obtain consistent estimators of the innovations. In a second step these estimated residuals can be used in order to levitate the problem of specifying the variance model in particular in the multi-output case. This is demonstrated in an ARCH setting, where it is proved that the estimated innovations can be used in place of the true innovations for testing in a linear least squares context in order to specify the structure of the ARCH model without changing the asymptotic distribution.Multivariate models, conditional heteroskedasticity, ARMA systems, subspace methods
Semiparametrically efficient rank-based inference for shape II. Optimal R-estimation of shape
A class of R-estimators based on the concepts of multivariate signed ranks
and the optimal rank-based tests developed in Hallin and Paindaveine [Ann.
Statist. 34 (2006)] is proposed for the estimation of the shape matrix of an
elliptical distribution. These R-estimators are root-n consistent under any
radial density g, without any moment assumptions, and semiparametrically
efficient at some prespecified density f. When based on normal scores, they are
uniformly more efficient than the traditional normal-theory estimator based on
empirical covariance matrices (the asymptotic normality of which, moreover,
requires finite moments of order four), irrespective of the actual underlying
elliptical density. They rely on an original rank-based version of Le Cam's
one-step methodology which avoids the unpleasant nonparametric estimation of
cross-information quantities that is generally required in the context of
R-estimation. Although they are not strictly affine-equivariant, they are shown
to be equivariant in a weak asymptotic sense. Simulations confirm their
feasibility and excellent finite-sample performances.Comment: Published at http://dx.doi.org/10.1214/009053606000000948 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Modified Whittle estimation of multilateral models on a lattice.
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d≥2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is the “edge effect”, which worsens with increasing d. The other is the possible difficulty of computing a continuous-frequency form of Whittle estimate or a time domain Gaussian maximum likelihood estimate, due mainly to the Jacobian term. This is especially a problem in “multilateral” models, which are naturally expressed in terms of lagged values in both directions for one or more of the d dimensions. An extension of the discrete-frequency Whittle estimate from the time series literature deals conveniently with the computational problem, but when subjected to a standard device for avoiding the edge effect has disastrous asymptotic performance, along with finite sample numerical drawbacks, the objective function lacking a minimum-distance interpretation and losing any global convexity properties. We overcome these problems by first optimizing a standard, guaranteed non-negative, discrete-frequency, Whittle function, without edge-effect correction, providing an estimate with a slow convergence rate, then improving this by a sequence of computationally convenient approximate Newton iterations using a modified, almost-unbiased periodogram, the desired asymptotic properties being achieved after finitely many steps. The asymptotic regime allows increase in both directions of all d dimensions, with the central limit theorem established after re-ordering as a triangular array. However our work offers something new for “unilateral” models also. When the data are non-Gaussian, asymptotic variances of all parameter estimates may be affected, and we propose consistent, non-negative definite estimates of the asymptotic variance matrix.Spatial data; Multilateral modelling; Whittle estimation; Edge effect; Consistent variance estimation;
The extremogram: A correlogram for extreme events
We consider a strictly stationary sequence of random vectors whose
finite-dimensional distributions are jointly regularly varying with some
positive index. This class of processes includes, among others, ARMA processes
with regularly varying noise, GARCH processes with normally or
Student-distributed noise and stochastic volatility models with regularly
varying multiplicative noise. We define an analog of the autocorrelation
function, the extremogram, which depends only on the extreme values in the
sequence. We also propose a natural estimator for the extremogram and study its
asymptotic properties under -mixing. We show asymptotic normality,
calculate the extremogram for various examples and consider spectral analysis
related to the extremogram.Comment: Published in at http://dx.doi.org/10.3150/09-BEJ213 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Modified Whittle Estimation of Multilateral Models on a Lattice
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d >= 2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is the "edge effect", which worsens with increasing d. The other is the possible difficulty of computing a continuous-frequency form of Whittle estimate or a time domain Gaussian maximum likelihood estimate, due mainly to the Jacobian term. This is especially a problem in "multilateral" models, which are naturally expressed in terms of lagged values in both directions for one or more of the d dimensions. An extension of the discrete-frequency Whittle estimate from the time series literature deals conveniently with the computational problem, but when subjected to a standard device for avoiding the edge effect has disastrous asymptotic performance, along with finite sample numerical drawbacks, the objective function lacking a minimum-distance interpretation and losing any global convexity properties. We overcome these problems by first optimizing a standard, guaranteed non-negative, discrete-frequency, Whittle function, without edge-effect correction, providing an estimate with a slow convergence rate, then improving this by a sequence of computationally convenient approximate Newton iterations using a modified, almost-unbiased periodogram, the desired asymptotic properties being achieved after finitely many steps. The asymptotic regime allows increase in both directions of all d dimensions, with the central limit theorem established after re-ordering as a triangular array. However our work offers something new for "unilateral" models also. When the data are non-Gaussian, asymptotic variances of all parameter estimates may be affected, and we propose consistent, non-negative definite estimates of the asymptotic variance matrix.spatial data, multilateral modelling, Whittle estimation, edge effect, consistent variance estimation
Asymptotic Properties of Pseudo Maximum Likelihood Estimates for Multiple Frequency I(1) Processes
In this paper we derive (weak) consistency and the asymptotic distribution of pseudo maximum likelihood estimates for multiple frequency I(1) processes. By multiple frequency I(1) processes we denote processes with unit roots at arbitrary points on the unit circle with the integration orders corresponding to these unit roots all equal to 1. The parameters corresponding to the cointegrating spaces at the different unit roots are estimated super-consistently and have a mixture of Brownian motions limiting distribution. All other parameters are asymptotically normally distributed and are estimated at the standard square root of T rate. The problem is formulated in the state space framework, using the canonical form and parameterization introduced by Bauer and Wagner (2002b). Therefore the analysis covers vector ARMA processes and is not restricted to autoregressive processes.state space representation; unit roots; cointegration; pseudo maximum likelihood estimation
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