Forecasting technique familiarity,satisfaction, usage,and application

A ten-year retrospective study of Mentzer and Cox (1984) was undertaken to answer the question 'Have sales forecasting practices changed over the past ten years?' A mail survey of 207 forecasting executives was employed to investigate this important question. Findings revealed both discrepancies and similarities between today's sales forecasting practices and those of ten years ago. One particular finding indicated greater reliance on and satisfaction with quantitative forecasting techniques today versus ten years ago. Another indicated that forecasting accuracy has not improved over the past ten years, even though the familiarity and usage of various sophisticated sales forecasting techniques have increased. Future research and managerial implications are discussed based on these and other findings.     

Forecasting commodity prices: GARCH,jumps, and mean reversion

In examining stochastic models for commodity prices, central questions often revolve around time‐varying trend, stochastic convenience yield and volatility, and mean reversion. This paper seeks to assess and compare alternative approaches to modelling these effects, with focus on forecast performance. Three specifications are considered: (i) random‐walk models with GARCH and normal or Student‐t innovations; (ii) Poisson‐based jump‐diffusion models with GARCH and normal or Student‐t innovations; and (iii) mean‐reverting models that allow for uncertainty in equilibrium price. Our empirical application makes use of aluminium spot and futures price series at daily and weekly frequencies. Results show: (i) models with stochastic convenience yield outperform all other competing models, and for all forecast horizons; (ii) the use of futures prices does not always yield lower forecast error values compared to the use of spot prices; and (iii) within the class of (G)ARCH random‐walk models, no model uniformly dominates the other. Copyright © 2008 John Wiley & Sons, Ltd.     

Influence of photoinduction on aminotransferase activity in biloxi soybean,Glycine max., L., Merr.

Summary Increasing number of photoinductive cycles markedly influenced the activity of 5 different aminotransferase systems as compared to non-inductive cycles. The activity was related to the requirement of the corresponding aminoacid synthesised during the process of photoinduction in the leaves.     

Forecasting capacity and capacity utilization in the U.S. aerospace industry

The forecasting of capacity and its utilization is particularly relevant in the aerospace industry because of long product delivery lead-times and the forward-pricing system. The objective of this paper is to develop a method for forecasting both the industry's capacity and its capacity utilization so that decision makers who must rely on this information may have policy guidance. The result shows that the aerospace industry's capacity expansion rate is closely tied to its present and recent past state of capacity utilization, and to anticipated changes in output. Output, in turn, can be predicted by using Five Year Defense Plans data and information on the cyclical nature of commercial business. Based on these findings, we were able to build an accurate model for forecasting aerospace industry capacity utilization.     

Forecasting donors and donations

The authors propose and evaluate a statistical model to make forecasts of donors, gifts and cumulative private donations to an educational fund. Within-season forecasts and confidence intervals are based on binomial counts of donors and a compound Poisson distribution for cumulative donations. Forecasts for the 1984-1985 fiscal year are based on data obtained in the previous two years. The effects of different promotional campaigns are studied.     

Forecasting quality and information

It is assumed that demand for information that subjectively appears to be relevant for forecasting improves forecasting quality. To study this hypothesis a number of forecasting experiments were conducted. Fifty managers from the housing business, from banking, and from a research institution were asked to forecast interest rates, using a Delphi process. They communicated via a computer system, and, to support their judgements, they had access to a data bank that was stored in the same system. Their communication with the system was automatically recorded. Part of the data collected in these experiments is used to study the existence of a relationship between information activities and forecasting results. A weak positive relationship is found if non-linear functions are tested, where information demand is corrected by those data retrievals that seem to have resulted from an inability to handle the information system. For further research a more general, albeit less informative, Boolean model is suggested.     

Forecasting Elections

In this paper we assess opinion polls, prediction markets, expert opinion and statistical modelling over a large number of US elections in order to determine which perform better in terms of forecasting outcomes. In line with existing literature, we bias‐correct opinion polls. We consider accuracy, bias and precision over different time horizons before an election, and we conclude that prediction markets appear to provide the most precise forecasts and are similar in terms of bias to opinion polls. We find that our statistical model struggles to provide competitive forecasts, while expert opinion appears to be of value. Finally we note that the forecast horizon matters; whereas prediction market forecasts tend to improve the nearer an election is, opinion polls appear to perform worse, while expert opinion performs consistently throughout. We thus contribute to the growing literature comparing election forecasts of polls and prediction markets. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting volatility

In this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model‐based volatility, and show that these volatility processes exhibit an extremely slow mean‐reverting behavior and possible long memory. For this reason, we explicitly model the near‐unit root behavior of volatility and construct median unbiased forecasts by approximating the finite‐sample forecast distribution using bootstrap methods. Furthermore, we produce prediction intervals for the next‐period implied volatility that provide important information about the uncertainty surrounding the point forecasts. Finally, we apply intercept corrections to forecasts from misspecified models which dramatically improve the accuracy of the volatility forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

Prior Information and ARIMA Forecasting

Using the method of ARIMA forecasting with benchmarks developed in this paper, it is possible to obtain forecasts which take into account the historical information of a series, captured by an ARIMA model (Box and Jenkins, 1970), as well as partial prior information about the forecasts. Prior information takes the form of benchmarks. These originate from the advice of experts, from forecasts of an annual econometric model or simply from pessimistic, realistic or optimistic scenarios contemplated by the analyst of the current economic situation. The benchmarks may represent annual levels to be achieved, neighbourhoods to be reached for a given time period, movements to be displayed or more generally any linear criteria to be satisfied by the forecasted values. The forecaster may then exercise his current economic evaluation and judgement to the fullest extent in deriving forecasts, since the laboriousness experienced without a systematic method is avoided.