Forecasting in large cointegrated processes

It is widely recognized that taking cointegration relationships into consideration is useful in forecasting cointegrated processes. However, there are a few practical problems when forecasting large cointegrated processes using the well‐known vector error correction model. First, it is hard to identify the cointegration rank in large models. Second, since the number of parameters to be estimated tends to be large relative to the sample size in large models, estimators will have large standard errors, and so will forecasts. The purpose of the present paper is to propose a new procedure for forecasting large cointegrated processes which is free from the above problems. In our Monte Carlo experiment, we find that our forecast gains accuracy when we work with a larger model as long as the ratio of the cointegration rank to the number of variables in the process is high. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Predicting small region sectoral responses to changes in aggregate economic activity: A time series approach

The purpose of this paper is to investigate the applicability of a contemporary time series forecasting technique, transfer function modeling, to the problem of forecasting sectoral employment levels in small regional economies. The specific sectoral employment levels to be forecast are manufacturing, durable manufacturing, non-durable manufacturing and non-manufacturing employment. Due to data constraints at the small region level, construction of traditional causal econometric models is often very difficult; thus time series approaches become particularly attractive. The results suggest that transfer function models using readily available national indicator series as drivers can provide more accurate forecasts of small region sectoral employment levels than univariate time series models.     

Long-term forecasting with innovation diffusion models: The impact of replacement purchases

The model presented in this paper integrates two distinct components of the demand for durable goods: adoptions and replacements. The adoption of a new product is modeled as an innovation diffusion process, using price and population as exogenous variables. Adopters are expected to eventually replace their old units of the product, with a probability which depends on the age of the owned unit, and other random factors such as overload, style-changes etc. It is shovn that the integration of adoption and replacement demand components in our model yields quality sales forecasts, not only under conditions where detailed data on replacement sales is available, but also when the forecaster's access is limited to total sales data and educated guesses on certain elements of the replacement process.     

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.     

A linear benchmark for forecasting GDP growth and inflation?

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.     

Bushfire rate-of-spread forecasting: Deterministic and statistical approaches to fire modelling

The three basic modelling approaches used to explain forest fire behaviour are theoretically, laboratory or empirically based. Results of all three approaches are reviewed, but it is noted that only the laboratory- and empirically based models have led to forecasting techniques that are in widespread use. These are the Rothermel model and the McArthur meters, respectively. Field tests designed to test the performance of these operational models were carried out in tropical grasslands. A preliminary analysis indicated that the Rothermel model overpredicted spread rates while the McArthur model underpredicted. To improve the forecast of bushfire rate of spread available to operational firefighting crews it is suggested that a time-variable parameter (TYP) recursive least squares algorithm can be used to assign weights to the respective models, with the weights recursively updated as information on fire-front location becomes available. Results of this methodology when applied to US Grasslands fire experiment data indicate that the quality of the input combined with a priori knowledge of the performance of the candidate models plays an important role in the performance of the TVP algorithm. With high-quality input data, the Rothermel model on its own outperformed the TVP algorithm, but with slightly inferior data both approaches were comparable. Though the use of all available data in a multiple linear regression produces a lower sum of squared errors than the recursive, time-variable weighting approach, or that of any single model, the uncertainties of data input and consequent changes in weighting coefficients during operational conditions suggest the use of the TVP algorithm approach.     

Detecting and Predicting Economic Accelerations,Recessions, and Normal Growth Periods in Real‐Time

The dichotomous characterization of the business cycle in recessions and expansions has been central in the literature over the last 50 years. However, there are various reasons to question the adequacy of this dichotomous, recession/expansion approach for our understanding of the business cycle dynamics, as well as for the prediction of future business cycle developments. In this context, the contribution of this paper to the literature is twofold. First, since a positive rate of growth at the level of economic activity can be considered as the normal scenario in modern economies due to both population and technological growth, it proposes a new non‐parametric algorithm for the detection and dating of economic acceleration periods, trend or normal growth periods, and economic recessions. Second, it uses an ordered probit framework for the estimation and forecasting of these three business cycle phases, applying an automatized model selection approach using monthly macroeconomic and financial data on the German economy. The empirical results show that this approach has superior out‐of‐sample properties under real‐time conditions compared to alternative probit models specified individually for the prediction of recessions and/or economic accelerations. Copyright © 2016 John Wiley & Sons, Ltd.