The effects of different choices of order for autoregressive approximation on the Gaussian likelihood estimates for ARMA models
type: Item
instance of: scholarly article
title: The effects of different choices of order for autoregressive approximation on the Gaussian likelihood estimates for ARMA models
zbMATH Open document ID: 1010.62082
author: M. O. Salau
published in: Statistical Papers
publication date: 2003-05-04T00:00:00Z
review text: This paper investigates, by means of Monte Carlo simulation, the effects of different choices of order for autoregressive approximation on the fully efficient parameter estimates for autoregressive moving average models. Four order selection criteria, AIC, BIC, HQ and PKK, were compared and different model structures with varying sample sizes were used to contrast the performance of the criteria. Some asymptotic results which provide a useful guide for assessing the performance of these criteria are presented. The results of this comparison show that there are marked differences in the accuracy implied using these alternative criteria in small sample situations and that it is preferable to apply BIC criterion, which leads to greater precision of Gaussian likelihood estimates, in such cases. Implications of the findings of this study for the estimation of time series models are highlighted.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 62F12
zbMATH DE Number: 1903719
zbMATH Keywords: best linear predictors
zbMATH Keywords: tables
MaRDI profile type: Publication
full work available at URL: s00362-002-0135-6
OpenAlex ID: W2017986433
cites work: A new look at the statistical model identification
cites work: Linear prediction by autoregressive model fitting in the time domain
cites work: Consistent autoregressive spectral estimates
cites work: Q4862306
cites work: Methods for Determining the Order of an Autoregressive-Moving Average Process: A Survey
cites work: ON SOME AMBIGUITIES ASSOCIATED WITH THE FITTING OF ARMA MODELS TO TIME SERIES
cites work: Q4884570
cites work: Q4039983
cites work: REGRESSION, AUTOREGRESSION MODELS
cites work: Q3830381
cites work: Q4195812
cites work: Recursive estimation of mixed autoregressive-moving average order
cites work: Asymptotic properties of time domain gaussian estimators
cites work: Prediction of multivariate time series by autoregressive model fitting
cites work: A new autoregressive spectrum analysis algorithm
cites work: ON THE RELATIONSHIP BETWEEN GENERALIZED LEAST SQUARES AND GAUSSIAN ESTIMATION OF VECTOR ARMA MODELS
cites work: Q4839356
cites work: Modeling by shortest data description
cites work: On the numerical implementation of the generalized least squares procedure for arma estimation
cites work: Estimating the dimension of a model
cites work: An optimal autoregressive spectral estimate
cites work: Bias of some commonly-used time series estimates
cites work: Factorization of the Covariance Generating Function of a Pure Moving Average Process
cites work: Some efficient computational procedures for high order ARMA models
DOI: 10.1007/S00362-002-0135-6
description: scientific article; zbMATH DE number 1903719