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

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

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

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, Nagar and 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 for reduced forms of correctly specified linear simultaneous equation models and they still remain unknown for the under and the over specified models. The purpose of this paper is to derive the matrices of the mean squared prediction error for both the underfitted and the overfitted models of 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

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

On the evolution of flow topology in turbulent Rayleigh-Bénard convection

Copyright 2016 AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing.Small-scale dynamics is the spirit of turbulence physics. It implicates many attributes of flow topology evolution, coherent structures, hairpin vorticity dynamics, and mechanism of the kinetic energy cascade. In this work, several dynamical aspects of the small-scale motions have been numerically studied in a framework of Rayleigh-Benard convection (RBC). To do so, direct numerical simulations have been carried out at two Rayleigh numbers Ra = 10(8) and 10(10), inside an air-filled rectangular cell of aspect ratio unity and pi span-wise open-ended distance. As a main feature, the average rate of the invariants of the velocity gradient tensor (Q(G), R-G) has displayed the so-calledPeer ReviewedPostprint (author's final draft

Agent based computational model of trust

This paper employs the methodology of Agent-Based Computational Economics (ACE) to investigate under what conditions trust can be viable in markets. The emergence and breakdown of trust is modeled in a context of multiple buyers and suppliers. Agents adapt their trust in a partner, the weight they attach to trust relative to profitability, and their own trustworthiness, modeled as a threshold of defection. Adaptation occurs on the basis of realized profit. Trust turns out to be viable under fairly general conditions