Parsimony and Its Importance in Time Series Forecasting

Johannes Ledolter; Bovas Abraham

doi:10.1080/00401706.1981.10487687

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Parsimony and Its Importance in Time Series Forecasting

Journal article

Peer reviewed

Parsimony and Its Importance in Time Series Forecasting

Johannes Ledolter and Bovas Abraham

Technometrics, Vol.23(4), pp.411-414

11/01/1981

DOI: 10.1080/00401706.1981.10487687

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Abstract

The effect of nonparsimonious time series models is studied by deriving the approximate variance of the one-step-ahead forecast error. Also, in a simulation experiment we show the loss in forecast accuracy that can result when a first-order moving-average model is approximated by a nonparsimonious autoregressive model.

Autoregression

Forecasting

Moving average

Parsimony

Simulation

Details

Title: Subtitle: Parsimony and Its Importance in Time Series Forecasting
Creators: Johannes Ledolter - University of Iowa
Bovas Abraham - University of Waterloo
Resource Type: Journal article
Publication Details: Technometrics, Vol.23(4), pp.411-414
Publisher: Taylor & Francis Group
DOI: 10.1080/00401706.1981.10487687
ISSN: 0040-1706
eISSN: 1537-2723
Language: English
Date published: 11/01/1981
Academic Unit: Business Analytics; Statistics and Actuarial Science
Record Identifier: 9984380495402771

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