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A methodology is developed for combining mean value forecasts using not only all the important statistics related to the past performance and the dependence of the individual forecasts, but also a rank ordering of the individual forecasts representing the belief of a decision maker about the future performance of the forecasts. The maximum likelihood combination of the forecasts turns out to be a weighted linear combination of the individual forecasts, where the weights are a function of the rank order of the forecasts, correlation coefficients between the forecasts, and relative entropy information measures between the individual forecasts and the actual values. These weights are assessed once in the most general case and once in a special case where the forecasts are normally distributed. The sensitivity of the weights is also investigated. A sample application of this method for predicting U.S. hog prices is also presented.
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This paper presents a methodology for producing a probability forecast of a turning point in U.S. economy using Composite Leading Indicators. This methodology is based on classical statistical decision theory and uses information-theoretic measurement to produce a probability. The methodology is flexible using as many historical data points as desired. This methodology is applied to producing probability forecasts of a downturn in U.S. economy in the 1970-1990 period. Four probability forecasts are produced using different amounts of information. The performance of these forecasts is evaluated using the actual downturn points and the scores measuring accuracy, calibration, and resolution. An indirect comparison of these forecasts with Diebold and Rudebusch's sequential probability recursion is also presented. It is shown that the performances of our best two models are statistically different from the performance of the three-consecutive-month decline model and are the same as the one for the best probit model. The probit model, however, is more conservative in its predictions than our two models.
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This paper discusses a regulatory technique that consists of the use of a controlled chain reaction to influence social and economic processes. It claims that this method was employed by Hungarian control agencies to further centralize the farm sector in the 1970s. Section I of the paper presents three versions of this technique. Section II shows how the institutional structure of Hungarian agriculture made the application of this technique possible. (JEL P21).
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There have been several studies that have investigated the effect of zoning on housing prices. One hypothesis is that the restrictiveness of zoning laws will vary with the monopoly power of a town. The degree of monopoly power varies with the number of towns in the urban area. Urban areas with few zoning jurisdictions are likely to have higher housing prices than more fragmented urban areas. Previous research on this topic has shown mixed results. The results in this article suggest that towns with more monopoly power do tend to have significantly higher housing prices than more fragmented urban areas.