Cost (or Price) Forecasting in the Face of Technological Advance

• Published in: Journal of the American Statistical Association Vol. 102; no. 477; pp. 28 - 43
• Main Authors: Harville, David A, Yashchin, Emmanuel
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.03.2007; American Statistical Association; Taylor & Francis Ltd
• Subjects: Accuracy; Analytical forecasting; Applications; Applications and Case Studies; Bayesian analysis; Bayesian inference; Bayesian method; Cost allocation; Cost estimates; Cost estimation models; Costs; Density distributions; Distribution theory; Estimate reliability; Exact sciences and technology; Forecasting models; Forecasts; General topics; Gibbs sampler; Hard disks; High technology; Inference from stochastic processes; time series analysis; Market theory; Markov chain Monte Carlo; Markovian processes; Mathematics; Matrices; Monte Carlo simulation; Moore's law; Price models; Probability and statistics; Probability theory and stochastic processes; Quarterly estimates; Research methodology; Sciences and techniques of general use; Statistical analysis; Statistical methods; Statistics; Technological change; Technological development; Technological forecasting; Bayes estimation; Stochastic model; Speed; Prior distribution; Gibbs sampling; Implementation; Statistical method; Gibbs sampler; Forecasting theory; Price; Moore's law; Markov chain Monte Carlo; Sampler; Distribution function; Relevant information; Bayesian inference; Application; Posterior distribution
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/016214506000001149

The problem considered involves forecasting the future costs of hard drives of various capacities and speeds of revolution or, more generally, forecasting the future costs of various quantifiably different versions of a commodity that is subject to technological advance. In the primary development, it is supposed that the data consist of past and present costs. A model is proposed in which the past, present, and future costs of each version are related with each other and also with the costs of the other versions. The model encompasses a stochastic version of an empirical relationship known as Moore's law. A forecasting methodology was developed by adopting a Bayesian approach and taking the prior distribution to be of a relatively tractable form. An implementation of the Gibbs sampler was devised for making draws from the posterior distribution of the future costs; the forecasts are based on those draws. The proposed methodology was used to obtain forecasts retrospectively from data accumulated (over a 5-year period) on the quarterly costs of hard drives. The accuracy of the longer-term forecasts compared favorably with those of certain benchmark forecasts, whereas the accuracy of the shorter-term forecasts compared less favorably. Greater accuracy can be achieved through enhancements to the proposed methodology that provide for the use of supplementary information (i.e., information that is relevant but not fully reflected in the past and present costs).