Abstract

This study assesses the accuracy of forecasts by industry branches. Such an investigation provides a perspective on the relative benefits of forecasting in different industries. Accuracy of forecasting is assessed by econometrically investigating expectations data on firms’ production drawn from surveys covering manufacturing. Such data is available for only few countries and few historical periods. We study U.S. data covering the 1980s and German data over the period from 1991 to 2018. We first present rankings of industries according to forecast accuracy for both countries. Then the historical gap between the two countries’ data set is put to use to assess the stability and the dynamics in the relevance of forecasting in different branches of industry. We identify several industries that – across time and place – are among the most (e.g., electric machinery) and least accurate forecasters (e.g., the food industry). By contrast in some industries forecasting performance appear to undergo noticeable changes over time: the reported evidence suggests that forecasting has lost some of its potential in the printing and textile industries while gaining over time in the nonelectric machinery and in the metals industry. The findings can help management to make decisions regarding the allocation of resources to forecasting.

Highlights

  • Business forecasting has a long history and a wide range of applications (Rötheli, 2007, Friedman, 2013)

  • When looking at the R 2 values across industries we find remarkable differences: the printing industry is the case with the best and the food industry the case with the worst performance in forecasting

  • Doing so we find that the R2 increases in these two industries over time: for both the metals industry as well as the nonelectric machinery industry this measure of forecasting accuracy shows an increase

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Summary

Introduction

Business forecasting has a long history and a wide range of applications (Rötheli, 2007, Friedman, 2013). A broad literature covers available techniques and applications (Newbury, 1953, Armstrong, 2001, Jain, 2004, Elliott, Graham & Timmermann, 2016, Fildes et al 2019). Concerning the actual use of forecasting techniques we have the benefit of findings from asking businesses what types of forecasting they practice (Newbury, 1953, Dalrymple, 1987, Peterson, 1993). The benefits of business forecasting depend on (i) the extent to which the product mix in an industry varies over the business cycle (expansion versus contraction), (ii) the extent to which the optimal size of the operation depends on the state of the economy, and (iii) on the accuracy of the forecasts available (Rötheli, 2018)

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