The Error of Prediction for a Simultaneous Equation Model

One of the most important functions of a simultaneous equation model is prediction the values of endogenous variables given the values of the predetermined variables and a lot of work has been done to estimate the accuracy of such predictions. Hooper and Zellner (1961) obtained the covariance matrix of the prediction error for unrestricted reduced form and Goldberger, Nagarand Odeh (1961) derived one for restricted reduced form. Properties of predictions for partially restricted reduced form have been analyzed by Amemiya (1966), Kakwani and Court (1972) and Nagar and Sahay (1978). The comparison of these estimators in the context of prediction has been carried on by Dhrymes (1973) and Park (1982). However all these derivations are made forreduced forms of correctly specified linear simultaneous equation models and they still remainunknown for the under and the over specified models. The purpose of this paper is to derive thematrices of the mean squared prediction error for both the underfitted and the overfitted modelsof unrestricted reduced form of a linear simultaneous equation system. The paper is organized as follows: Section 2 presents the basic model and its assumptions. Sections 3 and 4 derive the matrices of the mean squared prediction error for the underfitted and the overfitted models of unrestricted reduced form respectively. Section 5 gives the conclusions. An appendix contains the proofs of these derivations.prediction;misspecification;simultaneous equations

The optimal prediction simultaneous equation model selection

Notes on unsolved problems The standard linear simultaneous equation model is considered. a) The problem is to compare analytically the estimated reduced (unrestricted and derived) and the structural form of SEM from prediction point of view (for different estimators). Simulations show that estimated structural equations can outperform the reduced form equations. b) The problem is to obtain the matrices of the mean squared prediction error for the underfitted and the overfitted structural forms of a simultaneous equation system (Hale et al, 1980, "Finite Sample Analysis of Misspecification in Simultaneous Equation Models", Journal of the American Statistical Association 75, N.370, 418-427). c) The problem is to derive the unbiased information criterion for selecting the structural form of SEM with different number of parameters in each structural equation (Bedrick et al., 1994, "Model selection for multivariate regression in small samples" Biometrics 50, 226-231).

Electric cell voltage at etching and deposition of metals under an inhomogeneous constant magnetic field

The self-organized electric cell voltage of the physical circuit is calculated at etching and deposition of metals at the surface of a magnetized ferromagnetic electrode in an electrolyte without passing an external electrical current. This self-organized voltage arises due to the inhomogeneous distribution of concentration of the effectively dia- or paramagnetic cluster components of an electrolyte at the surface of a ferromagnetic electrode under the effect of inhomogeneous magnetostatic fields. The current density and Lorentz force are calculated in an electrolyte in the vicinity of the magnetized steel ball-shaped electrode. The Lorentz force causes the rotation of an electrolyte around the direction of an external magnetic field.Comment: 18 pages, 15 figure

The Optimal Prediction Simultaneous Equations Selection

This paper presents a method for selection of the optimal simultaneous equation system from a set of nested models under the condition of a small sample. The purpose of selection is to identify a model with the best prognostic possibilities. Multivariate AIC, BIC and AICC are used as the selection criteria. The selection properties of this method are investigated by Monte-Carlo simulations. They show that the structural form of system can outperform its reduced form for making predictions.criteria

Adaptive build-up and breakdown of trust: An agent based computational approach

This article employs Agent-Based Computational Economics (ACE) to investigate whether, and under what conditions, trust is viable in markets. The emergence and breakdown of trust is modeled in a context of multiple buyers and suppliers. Agents develop trust in a partner as a function of observed loyalty. They select partners on the basis of their trust in the partner and potential profit, with adaptive weights. On the basis of realized profits, they adapt the weight they attach to trust relative to profitability, and their own trustworthiness, modeled as a threshold of defection. Trust and loyalty turn out to be viable under fairly general conditions.Agent-based computational economics;Inter-firm relations;Transaction costs;Governance;Trust;Complex adaptive systems

Regular dependence of acoustic signals from dispersion at jet grinding ores

В умовах роботи струминних подрібнювальних установок виявлено акустичні закономірності зміни дисперсності матеріалів у процесі тонкого подрібнення. Запропоновано комплексні акустичні й технологічні критерії підвищення якості подрібненого продукту при зниженні питомих енерговитратВ условиях работы струйных измельчительных установок выявлены акустические закономерности изменения дисперсности материалов в процессе тонкого измельчения. Предложены комплексные акустические и технологические критерии повышения качества измельченного продукта при снижении удельных энергозатрат.In operating conditions of jet grinding installations acoustic laws of material dispersiveness change are revealed during thin grinding. Complex acoustic and technological criteria of ground product quality improvement are offered with specific power inputs decrease