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Identifier to cite or link to this item: https://hdl.handle.net/10086/14084

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Title
Asymptotic Prediction Mean Squared Error for Strongly Dependent Processes with Estimated Parameters
Creator
Creator Name
Katayama, Naoya Katayama, Naoya
片山, 直也 片山, 直也
カタヤマ, ナオヤ カタヤマ, ナオヤ
Affiliation
Affiliation Name
Hitotsubashi University
Editor
Contributor Name
社会科学における統計分析拠点構築 = Research Unit for Statistical Analysis in Social Sciences
Key Word
Mean-squared prediction errosrs
Long memory
Seasonality
Nonlinear least squares estimators
Convergence of moments
JEL
C22
C53
Abstract
In this paper we deal with the prediction theory of long memory processes. After investigating the general theory relating to convergence of moments of the nonlinear least squares estimators, we evaluate the asymptotic prediction mean squared error of two predictors. One is defined by using the estimator of the differencing parameter and the other is defined by using a fixed, known differencing parameter, which is, in other words, one parametric predictor of the seasonally integrated autoregressive moving average (SARIMA) models. In this paper, results do not impose the normality assumption and deal not only with stationary time series but also with nonstationary ones. The finite sample behavior is examined by simulations using the computer program S-PLUS in terms of the asymptotic theory.
Description
January 2004; revised September 2006
This paper is based on a portion of Chapter 3 of the author's Ph.D. thesis.
Publisher
Institute of Economic Research, Hitotsubashi University
Issued Date
2006-09
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Hi-Stat Discussion paper series ; No. d03-10
Relation
Related Identifier
URI
  http://21coe.ier.hit-u.ac.jp/english/index.html
Number of Pages
42
Sponsorship
21世紀COEプログラム = 21st-Century COE Program
Edition
revised September 2006
Appears in Collections
Hi-Stat Discussion paper series
URL
https://hdl.handle.net/10086/14084