Bayesian estimation procedure in multiprocess non-linear dynamic generalized model

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers arid is subject to abrupt changes in pattern. In this paper we extend the multiprocess dynamic generalized linear model to allow for a known non-linear parameter evolution and predictor functions. This is done by approximating the non-linear function by a linear function based on a first order Taylor series expansions. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.     

The numerical evaluation of the distribution function of a bilinear from to a quadratic from with econometric examples

This paper considers a general and computationally convenient method of evaluating the distribution function of statistics that are the ratio of a bilinear form to a quadratic form. Numerous Economemc applications of the method are given.     

A note on properties of spatial yule-walker estimators

For two-dimensional spatial autoregressive (AR) models, asymptotic properties of the spatial Yule-Walker (YW) estimators (Tjøstheim, 1978) are studied. These estimators although consistent, are shown to be asymptotically biased. Estimators from the first-order spatial bilateral AR model are looked at in more detail and the spatial YW estimators for this model are compared with the exact maximum likelihood estimators. Small sample properties of both estimators are also discussed briefly and some simulation results are presented.     

The coefficient of determination and its adjusted version in linear regression models

This article presents a comparative study of the efficiency properties of the coefficient of determination and its adjusted version in linear regression models when disturbances are not necessarily normal.     

Some subjective bayesian considerations in the selection of models

Some considerations relating to the post-data selection of models are discussed. These include some difficulties with orthodox theory, implementation of the likelihood principle, and Bayesian tests of hypotheses.     

A monte carlo comparison of tests based on the durbin-watson statistic with other autocorrelation tests in dynamic models

This paper examines the sampling properties of a number of serial correlation tests in dynamic linear models which include one or two lags of the dependent variable. Among the tests considered are the Durbin-Watson (DW) bounds test, modified versions of the DW proposed recently by King and Wu and Inder, Durbin's m test, Inder's point optimal test and a Hausman type test. Sampling designs include models with one or two lags of the dependent variable. The m, Hausman, and Inder's tests have the best performance, while Inder's modified DW test appears to be better than the other DW based tests. Results also suggest that tests are less powerful and more sensitive to design parameters in models with higher dynamics, with the DW-based tests being the most sensitive.     

Amemiya's generalized least squares and tests of overidentification in simultaneous equation models with qualitative or limited dependent variables

Amemiya's generalized least squares method for the estimation of simultaneous equation modeis with qualitative or limited dependent variables is known to be efficient relative to many popular two stage estimators. This note points out that test statistics for overidentification restrictions can be obtained as by-products of Amerniya's generalized least squares procedure. Amemiya's procedure is shown to be a minimum chisquare method. The Amemiya procedure is valuable both for efficient estimation and for model evaluation of such models.     

TAPAS: A modular framework to support reuse in scheduling software development

Industrial schedulers try to take into account more and more complex aspects of manufacturing and become, as a consequence, more difficult to use efficiently. We suggest a modular framework for scheduling in order to support the development of schedulers better adapted to the workshop they are used in. An extensive reuse of past developments is made possible by the definition of a powerful, yet generic, data model, together with a fully modular architecture and the use of standards, e.g. CORBA, MMS and STEP. Examples of the first schedulers developed within this framework in an industrial context are shown.     

Product Positioning and Pricing Under Production Cost Considerations*

A framework in a competitive environment is proposed that incorporates production cost and economies of scale in the problem of positioning a product for a market segment. The model facilitates the existence of a Nash equilibrium in prices and product positions. As such, firms can simultaneously choose prices and product positions for the segment. This result improves the traditional theory on equilibria points in prices and product positions where firms choose their product positions first and then set their prices. A sensitivity analysis demonstrates the effects of changes in the unit savings derived from economies of scale or the cost of furnishing a product with its attributes by one firm on the product positions, prices, and profits of all competing firms. More important, the paper examines the effect on prices and profits of competing firms when one of the firms repositions its product closer to the segment's ideal point. It is shown that under certain conditions, the profit of a firm may actually decrease as it redesigns its product closer to the segment's ideal point. These conditions assist management to identify the product design beyond which enhancements of the product would lead to lower profits because of increasing production costs. It is also shown that the price of this firm increases. Past research supports the idea that positioning a brand closer to the ideal point, given fixed product positions of competing firms, would lead to greater buyer preferences and eventually higher profits. The price and profits of the competing firm may increase or decrease. Conditions are derived under which a movement towards the segment's ideal point by one firm would lead to higher profits by the competing firm.