Estimation in ARMA models based on signed ranks


  • A. Kaaouachi
  • J. Allal

In this paper we develop an asymptotic theory for estimation based on signed ranks in the ARMA model when the innovation density is symmetrical. We provide two classes of estimators and we establish their asymptotic normality with the help of the asymptotic properties for serial signed rank statistics. Finally, we compare our procedure to the one of least-squares, and we illustrate the performance of the proposed estimators via a Monte Carlo study.

برای دانلود باید عضویت طلایی داشته باشید

برای دسترسی به متن کامل این مقاله و 23 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semiparametrically Efficient Inference Based on Signed Ranks in Symmetric Independent Component Models

We consider semiparametric location-scatter models for which the p-variate observation is obtained as X = ΛZ + µ, where µ is a p-vector, Λ is a full-rank p × p matrix, and the (unobserved) random p-vector Z has marginals that are centered and mutually independent but are otherwise unspecified. As in blind source separation and independent component analysis (ICA), the parameter of interest thro...

متن کامل

Adaptive Goodness - of - Fit Tests Based on Signed Ranks

Within the nonparametric regression model with unknown regression function l and independent, symmetric errors, a new multiscale signed rank statistic is introduced and a conditional multiple test of the simple hypothesis l = 0 against a nonparametric alternative is proposed. This test is distribution-free and exact for finite samples even in the heteroscedastic case. It adapts in a certain sen...

متن کامل

Robust Estimation for Arma Models

This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where ...

متن کامل

R-estimation for Arma Models

This paper is devoted to the R-estimation problem for the parameter of a stationary ARMA model. The asymptotic uniform linearity of a suitable vector of rank statistics leads to the asymptotic normality of √ n-consistent R-estimates resulting from the minimization of the norm of this vector. By using a discretized √ n-consistent preliminary estimate, we construct a new class of one-step R-estim...

متن کامل

Optimal Estimation of Multivariate ARMA Models

Autoregressive moving average (ARMA) models are a fundamental tool in time series analysis that offer intuitive modeling capability and efficient predictors. Unfortunately, the lack of globally optimal parameter estimation strategies for these models remains a problem: application studies often adopt the simpler autoregressive model that can be easily estimated by maximizing (a posteriori) like...

متن کامل

Optimal Instrumental Variables Estimation for ARMA Models

In this paper a new class of Instrumental Variables estimators for linear processes and in particular ARMA models is developed. Previously, IV estimators based on lagged observations as instruments have been used to account for unmodelled MA(q) errors in the estimation of the AR parameters. Here it is shown that these IV methods can be used to improve efficiency of linear time series estimators...

متن کامل

ذخیره در منابع من

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}

  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

دانلود متن کامل

برای دسترسی به متن کامل این مقاله و 23 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 2  شماره None

صفحات  207- 222

تاریخ انتشار 2003-11

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

کلمات کلیدی

میزبانی شده توسط پلتفرم ابری

copyright © 2015-2023