An aggregate sales model for consumer durables incorporating a time‐varying mean replacement age

Forecasting category or industry sales is a vital component of a company's planning and control activities. Sales for most mature durable product categories are dominated by replacement purchases. Previous sales models which explicitly incorporate a component of sales due to replacement assume there is an age distribution for replacements of existing units which remains constant over time. However, there is evidence that changes in factors such as product reliability/durability, price, repair costs, scrapping values, styling and economic conditions will result in changes in the mean replacement age of units. This paper develops a model for such time‐varying replacement behaviour and empirically tests it in the Australian automotive industry. Both longitudinal census data and the empirical analysis of the replacement sales model confirm that there has been a substantial increase in the average aggregate replacement age for motor vehicles over the past 20 years. Further, much of this variation could be explained by real price increases and a linear temporal trend. Consequently, the time‐varying model significantly outperformed previous models both in terms of fitting and forecasting the sales data. Copyright © 2001 John Wiley & Sons, Ltd.     

How successful are dynamic factor models at forecasting output and inflation? A meta‐analytic approach

This paper uses a meta‐analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro‐area output and for euro‐area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre‐selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well. Copyright © 2008 John Wiley & Sons, Ltd.     

Nonlinear predictor models

A complete dynamic model is introduced within the Bayesian framework. This includes the dynamic linear model and the normal discount Bayesian model as special cases and extends to some well-known models with nonlinear predictors. A number of practically important models are formulated and simple recurrence formulas, similar to those of Kalman, are used in the sequential estimation of the parameters. Finally, a number of practical examples and applications are given.     

Modelling the development of supply‐restricted telecommunications markets

A large proportion of the world telecommunications market can be characterized as supply restricted. In ITU (1999) official waiting lists numbered about 50 million worldwide with an average waiting time of two years. More than 100 countries had not eliminated the waiting list for telephone connections and hence a supply restricted market prevailed in all of these countries. Only about 25 countries have succeeded in eradicating their waiting list for basic telephone service. In terms of the pattern of diffusion, the subscriber's flow from waiting applicants to adopters is controlled by supply restrictions adding an important dimension that needs to be addressed when modeling and forecasting demand. An empirical analysis of the diffusion of main telephones in 46 supply‐restricted countries is presented to demonstrate the usefulness of a three‐stage Bass model that has been proposed to capture the dynamics of supply restrictions. We also compare the forecasting ability of different approaches to estimation when panel data are available. Copyright © 2001 John Wiley & Sons, Ltd.     

Short‐term forecasts of economic activity: Are fortnightly factors useful?

A short‐term mixed‐frequency model is proposed to estimate and forecast Italian economic activity fortnightly. We introduce a dynamic one‐factor model with three frequencies (quarterly, monthly, and fortnightly) by selecting indicators that show significant coincident and leading properties and are representative of both demand and supply. We conduct an out‐of‐sample forecasting exercise and compare the prediction errors of our model with those of alternative models that do not include fortnightly indicators. We find that high‐frequency indicators significantly improve the real‐time forecasts of Italian gross domestic product (GDP); this result suggests that models exploiting the information available at different lags and frequencies provide forecasting gains beyond those based on monthly variables alone. Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series.