Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures

A feature transformation method based on domain knowledge for arti?cial neural networks (ANNs) is proposed. The method of feature transformation based on domain knowledge converts continuous values into discrete values in accordance with the knowledge of experts in speci?c application domains. This approach effectively ?lters data, trains the classi?er, and extracts the rules from the classi?er. In addition, it reduces the dimensionality of the feature space, which not only decreases the cost and time in the operation but also enhances the generalizability of the classi?er. The experimental results of the proposed approach will be compared and tested statistically with the results of the linear transformation method. The results show that the method of feature transformation based on domain knowledge outperforms the linear transformation in modelling of ANNs. Copyright © 2004 John Wiley & Sons, Ltd.     

Educational attainment and cohort size

"We argue that the postwar baby boom [in the United States] caused substantial fluctuations in both the economic rewards to education and educational attainment over the last 3 decades. If substitutability between young and old workers diminishes with education, the present value of lifetime earnings for a boom cohort is depressed more for highly educated workers, reducing incentives for educational attainment. The opposite is true for pre- and postboom cohorts. The diminishing substitutability hypothesis explains the declines in both the returns to college and college completion rates in the 1970s and predicts a substantial increase in educational attainment for postboomers."     

Preliminary test estimators of the parameters of simple linear model with measurement error

In this paper, we discuss the properties of preliminary test estimators (PTE) of the parameters of simple linear model with measurement error (ME model) when the slope of the linear model is suspected to be zero. Expressions of the bias, MSE and efficiencies are obtained under conditional as well as unconditional situations with known reliability coefficient. Conditional model results are compared to the standard model without measurement error. We also provide the unconditional model analysis in finite samples. Asymptotic theory under local alternatives is developed when the variance of measurement error or the ratio of the variance of the model error relative to the variance of the measurement error is known. Asymptotic expressions of bias and MSE of the estimators along with their efficiencies are obtained. In every case, it is shown that the measurement error tend to increase the variability of the estimators compared to the estimators without measurement error. Graphs and tables are provided to see these results and to determine optimum level of significance for minimum guaranteed efficiency. Received October 2001 RID="*" ID="*"  A. K. Md. E. Saleh is a Distinguished Research Professor and H. M. Kim is a Ph.D. candidate in the School of Mathematics and Statistics, Carleton University, Ottawa. Acknowledgment. The authors gratefully acknowledge the constructive suggestion of the referees to improve the paper. The research is supported by NSERC grant A3088.     

Product attributes and models of multiple discreteness

Demand for product characteristics is examined within the context of models that allow for both corner and interior solutions corresponding to zero and non-zero demand. Product attribute information is associated with marginal utility and curvature (satiation) parameters of various utility functions. Empirical applications demonstrate the need for incorporating characteristics in a fairly general way. We also compare our approach to an ideal point and pure Lancasterian versions of our nonlinear utility model. The data support our model over either the ideal point or Lancasterian variants.     

The Measurement of Consumption Efficiency Considering the Discrete Choice of Consumers

In this study we propose a mathematical definition of the consumption efficiency of multi-attribute products in the price–quality space. A new model, the discrete Range Adjusted Measure (RAM) model, is suggested as an empirical tool to measure the level of consumption efficiency. We further discuss the effect of consumption efficiency on the innovation incentive. Empirical work is made for the mobile phone market. We expect that the consumption efficiency concept will contribute to the extension of the traditional framework of production efficiency analysis on the one hand and to the understanding of the nature of innovation in a technology-intensive market on the other hand.JEL Classification: C67, D11, D12, D21     

A two-stage stratified Warner’s randomized response model using optimal allocation

This paper applies Kim and Wardes (2004) stratified Warners randomized response model to Mangat and Singhs (1990) two-stage randomized response model. The proposed stratified randomized response model has an optimal allocation and a large gain in precision. Hence, the estimator based on the proposed method is more efficient than Kim and Wardes (2004) and Mangat and Singhs (1990) estimators under the conditions presented in both the case of completely truthful reporting and that of not completely truthful reporting by the respondents.     

The subjective expected utility hypothesis and revealed preference

Summary A choice behavior of a decision maker is said to satisfy the subjective expected utility hypothesis if there exist a utility and a subjective probability such that a decision maker chooses acts with the highest expected utility. We present a revealed preference characterization of choice behavior that is consistent with the subjective expected utility hypothesis. Our result applies to any state space and compact sets of prizes and observations (either finite or infinite).