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Forecasting with nonlinear time series model: A Monte-Carlo bootstrap approach


N. Ekhosuehi
S. E. Omosigho

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


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eISSN: 3057-3629
print ISSN: 0855-0395