The effect of adverse publicity on sales: A case study

This paper estimates lost sales resulting from adverse publicity for a small company soon after it began selling solar hot-water units. Ordinary least squares analysis generates estimates from seasonally adjusted data. To capture the structural shift in sales, alternative series of dummy variables are tested along with other explanatory variables. The ‘best’ of the equations is reported, and the outcome of the court suit is discussed.     

Euro and FIBOR interest rates: A continuous time modelling analysis

The introduction of the Euro in January 1999 and the new reference interest rate EURIBOR® which is widely used as the underlying interest rate for Euro denominated derivative contracts have opened up a new area of research in international financial markets. In this paper we estimate single factor models using daily EURIBOR® and FIBOR interest rate data. We also estimate a model allowing a level-GARCH specification and a two factor model. We find evidence of level-volatility effects in both rates.     

A TEST OF BAYESIAN LEARNING FROM FARMER TRIALS OF NEW WHEAT VARIETIES*

In this study, elicited estimates of farmers' subjective beliefs about the mean and variance of wheat variety yields were used to test propositions about Bayesian learning developed in the recent literature on innovation adoption. A series of empirical tests of the Bayesian adoption model were conducted using beliefs elicited from farm surveys conducted in 1982, 1983 and 1984. The results of the analysis neither confirm nor reject the Bayesian approach as a model of how farmers revise subjective beliefs, but do raise serious doubts about its realism, and suggest some issues requiring further investigation. Shortcomings in the elicitation techniques are discussed and the assumptions of the Bayesian model are reviewed.     

Goal programming solutions to transportation problems with variable supply and demand requirements

This article deals with goal programming as an aid to resolving transportation problems. The objective of this study is to demonstrate the use of goal programming as an approach to reducing total cost in transportation problems that possess variable supply and demand requirements. A generalized goal programming model is formulated and applied to a series of transportation problem situations based on the empirical data provided by a medium-size trucking firm. Comparison of the results demonstrates the cost advantage of using the goal programming model in place of the transportation method and integer interval linear programming.