ARIMA models and the Box Jenkins methodology

A Chapter in the book Applied Econometrics. The purpose of this paper is to study the Box-Jenkins methodology to ARIMA models and determine the reasons why in empirical tests it is found that the post-sample forecasting

accuracy of such models is worse than much simpler time series methods. It is concluded that the major problem is the way of making the series stationary in its mean (i.e., the method of

differencing) that has been proposed by Box and Jenkins. If alternative approaches are utilized to remove and extrapolate the trend in the data, ARMA models outperform the corresponding methods involved in the great majority of cases. In addition it is shown that using ARMA models to seasonally adjusted data slightly improves post-sample accuracies while simplifying the use of ARMA models. It is also confirmed that transformations slightly improve post-sample forecasting accuracy, particularly for long forecasting horizons. Finally,

it is demonstrated that AR(1) and AR(2), or their combination, produce as accurate postsample results as those found through the application of the Box-Jenkins methodology.

Website:

https://he.palgrave.com/page/detail/Applied-Econometrics/?K=9781137415479

Date (Creation): 2015-10-01

Date (Publication): 2015-10-01

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Edition: third

Citation identifier: https://www.mica-project.eu/ / MICA_B2-13
MICA WP3 2017-09-25T10:33:00 Record extracted from Batch 2 spreadsheet

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Hellenic Open University			Resource provider

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