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Sales forecasting using multi‐equation transfer function models
Author(s) -
Liu LonMu
Publication year - 1987
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980060402
Subject(s) - univariate , autoregressive integrated moving average , residual , econometrics , computer science , multivariate statistics , probabilistic forecasting , variable (mathematics) , statistics , time series , mathematics , artificial intelligence , machine learning , algorithm , mathematical analysis , probabilistic logic
The purpose of this study is first, to demonstrate how multivariate forecasting models can be effectively used to generate high performance forecasts for typical business applications. Second, this study compares the forecasts generated by a simultaneous transfer function model (STF) model and a white noise regression model with that of a univariate ARIMA model. The accuracy of these forecasting models is judged using their residual variances and forecasting errors in a post‐sample period. It is found that ignoring the residual serial correlation can greatly degrade the forecasting performance of a multi‐variable model, and in some situations, cause a multi‐variable model to perform inferior to a univariate ARIMA model. This paper also demonstrates how a forecaster can use an STF model to compute both the multi‐step ahead forecasts and their variances easily.