Suppose we observe an ergodic Markov chain on the real line, with a parametric model for the autoregression function, i.e. the conditional mean of the transition distribution. If one specifies, in addition, a parametric model for the conditional variance, one can define a simple estimator for the pa
Estimation of the index parameter for autoregressive data using the estimated innovations
β Scribed by Michael R. Allen; Somnath Datta
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 126 KB
- Volume
- 41
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
β¦ Synopsis
In this paper we consider an invertible autoregressive process where the innovations (errors) are i.i.d. satisfying a tail regularity condition. The problem of estimation of the index of regular variation based on a ΓΏnite realization of the time series is addressed. We propose the use of a recently developed estimator of with the data values replaced by residuals obtained from the model. Consistency and asymptotic normality of the resulting estimator are established and its performance is compared with the original estimator calculated at the data values.
π SIMILAR VOLUMES
A closed-form expression for the exponential rate of an estimator in the Gaussian AR(1) process is obtained. This shows that the exponential rates of several famous estimators are all identical. Further it is shown that mean-correction does not affect the large deviation asymptotics. (~
In this paper are presented four convenient methods for determination of the mean residence time and the axial dispersion coefficient of a flow system by analysis of data obtained by means of the imperfect tracer pulse method. The analysis is based upon numerical evaluation of the transfer function
We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have convergence problems (Puthenpura and Sinha, 1986) [11], we work out modified maximum