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Published:
Apr 12, 2016DOI:
10.4314/just.v35i3.5Keywords:
Bootstrap Forecasting LSTAR Monte-Carlo Monte-Carlo Bootstrap
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The copyright of a submitted article is only transferred to the publishers if and when the article is accepted for publication. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, electrostatic, mechanical, photocopying, recording or otherwise without the prior written permission of the publishers.
Main Article Content
Forecasting with nonlinear time series model: A Monte-Carlo bootstrap approach
Abstract
In this paper, we propose a new method of forecasting with nonlinear time series model using Monte-Carlo Bootstrap method. This new method gives better result in terms of forecast root mean squared error (RMSE) when compared with the traditional Bootstrap method and Monte-Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition autoregressive (LSTAR) model. We illustrate this new method using some simulation experiments.
Keywords: Bootstrap, Forecasting, LSTAR, Monte-Carlo, Monte-Carlo Bootstrap
