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Journal of Time Series Econometrics

Editor-in-Chief: Hidalgo, Javier

CiteScore 2018: 0.20

SCImago Journal Rank (SJR) 2018: 0.323
Source Normalized Impact per Paper (SNIP) 2018: 0.291

Mathematical Citation Quotient (MCQ) 2018: 0.03

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Estimation Bias and Feasible Conditional Forecasts from the First-Order Moving Average Model

Yong Bao
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  • Department of Economics, Purdue University, 403 W. State Street, West Lafayette, IN 47907, USA
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/ Ru Zhang
Published Online: 2013-07-26 | DOI: https://doi.org/10.1515/jtse-2013-0015


The quasi-maximum likelihood estimator (QMLE) of parameters in the first-order moving average model can be biased in finite samples. We develop the second-order analytical bias of the QMLE and investigate whether this estimation bias can lead to biased feasible optimal forecasts conditional on the available sample observations. We find that the feasible multiple-step-ahead forecasts are unbiased under any nonnormal distribution, and the one-step-ahead forecast is unbiased under symmetric distributions.

Keywords: bias; moving average; feasible forecasts

JEL Classification: C22; C53


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About the article

Published Online: 2013-07-26

Citation Information: Journal of Time Series Econometrics, Volume 6, Issue 1, Pages 63–80, ISSN (Online) 1941-1928, ISSN (Print) 2194-6507, DOI: https://doi.org/10.1515/jtse-2013-0015.

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