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Activity Number: 255 - Contributed Poster Presentations: Section on Statistical Computing
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #304810
Title: Bootstrapping Transfer Function Models
Author(s): Maher Qumsiyeh* and Didiere Hirwantwari
Companies: and University of Dayton
Keywords: ARIMA; Bootstrap; Transfer Function Models; Simulation

Box Jenkins Method (ARIMA) have proven to be very useful in forecasting with a good degree of accuracy. However, many assumptions are made in these models that can affect the accuracy when the assumptions are not met. The bootstrap method introduced by Efron (1979) tries to circumvent these assumptions and to find better estimates for the parameters and better forecasts. In this work we will show how well the bootstrap performs in an autoregressive transfer function model. All analysis and programming was done using the statistical software SAS.

Authors who are presenting talks have a * after their name.

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