Using the bootstrap for improved ARIMA model identification

This paper presents a new method of identifying ARIMA time-series models. The authors use the bootstrap technique in estimating the distribution of sample autocorrelations both separately and in a simultaneous inference setting. The bootstrap has the advantage of being nonparametric and thus free of reliance on asymptotic normality, which may not hold for short or medium-size series. The simultaneous procedure is unique, as it has no feasible parametric alternatives. An application to exchange rates illustrates the present methodology. In the example chosen, the authors are able to produce better forecasts using the model identified via the bootstrap technique.