By David A. Kendrick
The facility to conceptualize an fiscal challenge verbally, to formulate it as a mathematical version, after which symbolize the maths in software program in order that the version should be solved on a working laptop or computer is an important ability for economists. Computational Economics comprises famous models--and a few brand-new ones--designed to aid scholars stream from verbal to mathematical to computational representations in fiscal modeling. The authors' concentration, in spite of the fact that, is not only on fixing the types, but additionally on constructing the power to switch them to mirror one's curiosity and viewpoint. the result's a e-book that allows scholars to be artistic in constructing types which are appropriate to the industrial difficulties in their instances. not like different computational economics textbooks, this e-book is prepared round financial issues, between them macroeconomics, microeconomics, and finance. The authors hire a variety of software program systems--including MATLAB, Mathematica, GAMS, the nonlinear programming solver in Excel, and the database platforms in Access--to allow scholars to exploit the main positive approach. The ebook progresses from particularly uncomplicated types to extra advanced ones, and contains appendices at the fine details of working each one program.The e-book is meant to be used via complicated undergraduates economists or even, as a primary publicity to computational economics, by way of graduate scholars. prepared by way of monetary themes Progresses from easy to extra complicated types contains directions on various software program structures Encourages customization and creativity
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This is done in an Excel spreadsheet using the Solver that we first used in the growth model. The chapter begins with an introduction to neural nets followed by the specification of an automobile stock price model. Then we will introduce the data that is used in the model, the representation of the model in Excel and the use of the Excel Solver to find the best parameter values. 1. Neural Nets Models Neural networks (or, more properly, artificial neural networks) are inspired by, or up to a point analogous to, natural neural networks.
Input layer neurons receive data (“signals”) from outside and in general transmit them to the next layer without processing them. Output layer neurons return data to the outside, and are sometimes set to apply their combination functions only. The learning process of the network consists of choosing values of the weights so as to achieve a desired mapping from inputs to outputs. This is done by feeding the network with a set of inputs, comparing the output (or outputs, in case of having more than one network output) to a known target, computing the corresponding error and sometimes applying an error function.
Now we are ready for the optimization problem. 6 The Solver Dialog Box This dialog box indicates the optimization problem is to minimize the value in cell C15 (which on inspection is set equal to M35 which in turn is the sum of the elements in the Norm column). As was discussed earlier, the Excel Solver uses nonlinear optimization methods (Newton method or conjugate gradient method - see Appendix F). The optimization is done by changing the elements in the cells D5:D12 until the minimum of the function is obtained.