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The PMI, the T‐Bill and Inventories: A Comparative Analysis of Neural Network and Regression Forecasts
Author(s) -
Larrain Maurice
Publication year - 2007
Publication title -
journal of supply chain management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.75
H-Index - 92
eISSN - 1745-493X
pISSN - 1523-2409
DOI - 10.1111/j.1745-493x.2007.00030.x
Subject(s) - lagging , artificial neural network , econometrics , term (time) , regression analysis , regression , computer science , statistics , economics , artificial intelligence , mathematics , machine learning , physics , quantum mechanics
SUMMARY The PMI is widely used as an indicator of economic trends, and as a short‐term forecaster of several important lagging output variables. The PMI's importance has led to several attempts at forecasting its direction. This research builds on this foundation by attempting to forecast the PMI through the use of regression and neural network methodology. Findings indicate that short‐term interest rates lead and forecast changes in the PMI by 10 months. Additionally, the paper focuses on the PMI as a predictor. Of the many variables the PMI can fit or predict, results reveal inventories as the most relevant example for supply chain managers, showing the PMI is a predictor of real inventory changes 8 months out.