Optimal price regulation in a growth model with monopolistic suppliers of intermediate goods

In this paper we investigate the trade-off faced by regulators who must set a price for an intermediate good somewhere between the marginal cost and the monopoly price. We utilize a growth model with monopolistic suppliers of intermediate goods. Investment in innovation is required to produce a new intermediate good. Marginal cost pricing deters innovation, while monopoly pricing maximizes innovation and economic growth at the cost of some static inefficiency. We demonstrate the existence of a second-best price above the marginal cost but below the monopoly price, which maximizes consumer welfare. Simulation results suggest that substantial reductions in consumption, production, growth, and welfare occur where regulators focus on static efficiency issues by setting prices at or near marginal cost.

Measurement of glass-rubber transition temperature of skim milk powder by static mechanical test

Stickiness behavior of skim milk powder was investigated based on the mechanical property of the material during the glass-rubber transition. A thermally controlled device was developed for the static mechanical test. This device was attached to a texture analyzer, and skim milk powder, which was used as a model sample, was tested for its glass-rubber transition temperature (Tg-r) using static compression technique (creep test). Changes in compression probe distance as a function of temperature were recorded. Tg-r was determined, in the region where changes in the probe distance were observed, by using linear regression technique. The effect of sample quantity, compression force, and heating rate on the determination of Tg-r was investigated. All these parameters significantly influenced the Tg-r determination (p < 0.05). The Tg-r of skim milk powder measured by this novel technique was found closely correlated to its glass transition temperature (T-g) measured by DSC.

Vector Error-Correction Models in a consumer packaged goods category forecasting decision support system

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the shifts and still provide the other advantages of the BVAR is a Bayesian Vector Error-Correction Model (BVECM). We present the mechanics of extending the DSS to move from a BVAR model to the BVECM model for the category management problem. Several additional iterative steps are required in the DSS to allow the decision maker to arrive at the best forecast possible. The revised marketing DSS framework and model fitting procedures are described. Validation is conducted on a sample problem.