HIGHER‐ORDER ASYMPTOTIC PROPERTIES OF A WEIGHTED ESTIMATOR FOR GAUSSIAN ARMA PROCESSES

Research output: Contribution to journalArticle

Abstract

Abstract. Let {Xt} be a Gaussian ARMA process with spectral density fθ(Λ), where θ is an unknown parameter. To estimate θ, we propose an estimator θCw of the Bayes type. Since our standpoint in this paper is different from Bayes's original approach, we call it a weighted estimator. We then investigate various higher‐order asymptotic properties of θCw. It is shown that θCw is second‐order asymptotically efficient in the class of second‐order median unbiased estimators. Furthermore, if we confine our discussions to an appropriate class D of estimators, we can show that θCw is third‐order asymptotically efficient in D. We also investigate the Edgeworth expansion of a transformation of θCw. We can then give the transformation of θCw which makes the second‐order part of the Edgeworth expansion vanish. Finally we consider the problem of testing a simple hypothesis H:θ=θo against the alternative A:θ#θo. For this problem we propose a class of tests δA which are based on the weighted estimator. We derive the X2 type asymptotic expansion of the distribution of S (ζδA) under the sequence of alternatives An:θ=θo+εn1/2, ε > 0. We can then compare the local powers of various tests on the basis of their asymptotic expansions.

Original languageEnglish
Pages (from-to)83-93
Number of pages11
JournalJournal of Time Series Analysis
Volume12
Issue number1
DOIs
Publication statusPublished - 1991
Externally publishedYes

Fingerprint

ARMA Process
Asymptotic Properties
Estimator
Edgeworth Expansion
Bayes
Asymptotic Expansion
Local Power
Power of Test
Spectral density
Unbiased estimator
Alternatives
Spectral Density
Gaussian Process
Unknown Parameters
Vanish
Testing
ARMA process
Asymptotic properties
Estimate
Class

Keywords

  • Edgeworth expansion
  • Fisher's Z transformation
  • Gaussian ARMA processes:
  • higher‐order efficiency
  • normalizing transformation
  • power comparison
  • spectral density:
  • weighted estimator:

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

HIGHER‐ORDER ASYMPTOTIC PROPERTIES OF A WEIGHTED ESTIMATOR FOR GAUSSIAN ARMA PROCESSES. / Swe, Myint; Taniguchi, Masanobu.

In: Journal of Time Series Analysis, Vol. 12, No. 1, 1991, p. 83-93.

Research output: Contribution to journalArticle

@article{a0aa1abc880b4dcf8bd20db03b5012e7,
title = "HIGHER‐ORDER ASYMPTOTIC PROPERTIES OF A WEIGHTED ESTIMATOR FOR GAUSSIAN ARMA PROCESSES",
abstract = "Abstract. Let {Xt} be a Gaussian ARMA process with spectral density fθ(Λ), where θ is an unknown parameter. To estimate θ, we propose an estimator θCw of the Bayes type. Since our standpoint in this paper is different from Bayes's original approach, we call it a weighted estimator. We then investigate various higher‐order asymptotic properties of θCw. It is shown that θCw is second‐order asymptotically efficient in the class of second‐order median unbiased estimators. Furthermore, if we confine our discussions to an appropriate class D of estimators, we can show that θCw is third‐order asymptotically efficient in D. We also investigate the Edgeworth expansion of a transformation of θCw. We can then give the transformation of θCw which makes the second‐order part of the Edgeworth expansion vanish. Finally we consider the problem of testing a simple hypothesis H:θ=θo against the alternative A:θ#θo. For this problem we propose a class of tests δA which are based on the weighted estimator. We derive the X2 type asymptotic expansion of the distribution of S (ζδA) under the sequence of alternatives An:θ=θo+εn1/2, ε > 0. We can then compare the local powers of various tests on the basis of their asymptotic expansions.",
keywords = "Edgeworth expansion, Fisher's Z transformation, Gaussian ARMA processes:, higher‐order efficiency, normalizing transformation, power comparison, spectral density:, weighted estimator:",
author = "Myint Swe and Masanobu Taniguchi",
year = "1991",
doi = "10.1111/j.1467-9892.1991.tb00070.x",
language = "English",
volume = "12",
pages = "83--93",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "1",

}

TY - JOUR

T1 - HIGHER‐ORDER ASYMPTOTIC PROPERTIES OF A WEIGHTED ESTIMATOR FOR GAUSSIAN ARMA PROCESSES

AU - Swe, Myint

AU - Taniguchi, Masanobu

PY - 1991

Y1 - 1991

N2 - Abstract. Let {Xt} be a Gaussian ARMA process with spectral density fθ(Λ), where θ is an unknown parameter. To estimate θ, we propose an estimator θCw of the Bayes type. Since our standpoint in this paper is different from Bayes's original approach, we call it a weighted estimator. We then investigate various higher‐order asymptotic properties of θCw. It is shown that θCw is second‐order asymptotically efficient in the class of second‐order median unbiased estimators. Furthermore, if we confine our discussions to an appropriate class D of estimators, we can show that θCw is third‐order asymptotically efficient in D. We also investigate the Edgeworth expansion of a transformation of θCw. We can then give the transformation of θCw which makes the second‐order part of the Edgeworth expansion vanish. Finally we consider the problem of testing a simple hypothesis H:θ=θo against the alternative A:θ#θo. For this problem we propose a class of tests δA which are based on the weighted estimator. We derive the X2 type asymptotic expansion of the distribution of S (ζδA) under the sequence of alternatives An:θ=θo+εn1/2, ε > 0. We can then compare the local powers of various tests on the basis of their asymptotic expansions.

AB - Abstract. Let {Xt} be a Gaussian ARMA process with spectral density fθ(Λ), where θ is an unknown parameter. To estimate θ, we propose an estimator θCw of the Bayes type. Since our standpoint in this paper is different from Bayes's original approach, we call it a weighted estimator. We then investigate various higher‐order asymptotic properties of θCw. It is shown that θCw is second‐order asymptotically efficient in the class of second‐order median unbiased estimators. Furthermore, if we confine our discussions to an appropriate class D of estimators, we can show that θCw is third‐order asymptotically efficient in D. We also investigate the Edgeworth expansion of a transformation of θCw. We can then give the transformation of θCw which makes the second‐order part of the Edgeworth expansion vanish. Finally we consider the problem of testing a simple hypothesis H:θ=θo against the alternative A:θ#θo. For this problem we propose a class of tests δA which are based on the weighted estimator. We derive the X2 type asymptotic expansion of the distribution of S (ζδA) under the sequence of alternatives An:θ=θo+εn1/2, ε > 0. We can then compare the local powers of various tests on the basis of their asymptotic expansions.

KW - Edgeworth expansion

KW - Fisher's Z transformation

KW - Gaussian ARMA processes:

KW - higher‐order efficiency

KW - normalizing transformation

KW - power comparison

KW - spectral density:

KW - weighted estimator:

UR - http://www.scopus.com/inward/record.url?scp=84981389363&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84981389363&partnerID=8YFLogxK

U2 - 10.1111/j.1467-9892.1991.tb00070.x

DO - 10.1111/j.1467-9892.1991.tb00070.x

M3 - Article

AN - SCOPUS:84981389363

VL - 12

SP - 83

EP - 93

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 1

ER -