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Applied Computational Economics and Finance

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Author Info

  • Mario J. Miranda

    ()
    (Ohio State University)

  • Paul L. Fackler

    ()
    (North Carolina State University)

Abstract

This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. The examples are drawn from a wide range of subspecialties of economics and finance, with particular emphasis on problems in agricultural and resource economics, macroeconomics, and finance. The book also provides an extensive Web-site library of computer utilities and demonstration programs. The book is divided into two parts. The first part develops basic numerical methods, including linear and nonlinear equation methods, complementarity methods, finite-dimensional optimization, numerical integration and differentiation, and function approximation. The second part presents methods for solving dynamic stochastic models in economics and finance, including dynamic programming, rational expectations, and arbitrage pricing models in discrete and continuous time. The book uses MATLAB to illustrate the algorithms and includes a utilities toolbox to help readers develop their own computational economics applications.

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Bibliographic Info

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This book is provided by The MIT Press in its series MIT Press Books with number 0262633094 and published in 2004.

Volume: 1
Edition: 1
ISBN: 0-262-63309-4
Handle: RePEc:mtp:titles:0262633094

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Web page: http://mitpress.mit.edu

Related research

Keywords: computational economics; numerical methods; dynamic stochastic models;

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Cited by:
  1. Philip Bunn & Colin Ellis, 2012. "How do Individual UK Producer Prices Behave?," Economic Journal, Royal Economic Society, vol. 122(558), pages F16-F34, 02.
  2. Hwang, In Chang, 2014. "A recursive method for solving a climate-economy model: value function iterations with logarithmic approximations," MPRA Paper 54782, University Library of Munich, Germany.
  3. Rausser, Gordon C. & Balson, William & Stevens, Reid, 2009. "Centralized Clearing for Over-the-Counter Derivatives," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt494976mk, Department of Agricultural & Resource Economics, UC Berkeley.
  4. Pierre-André Jouvet & Elodie Lecadre & Caroline Orset, 2011. "Irreversible investment, uncertainty, and ambiguity: the case of bioenergy sector," Working Papers 2011/01, INRA, Economie Publique.
  5. Hwang, In Chang & Reynes, Frederic & Tol, Richard, 2014. "The effect of learning on climate policy under fat-tailed uncertainty," MPRA Paper 53681, University Library of Munich, Germany.
  6. Mosiño, Alejandro, 2012. "Producing energy in a stochastic environment: Switching from non-renewable to renewable resources," Resource and Energy Economics, Elsevier, vol. 34(4), pages 413-430.
  7. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex.
  8. Tchumtchoua, Sylvie & Cotterill, Ronald, 2010. "Optimal Brand and Generic Advertising Policies in a Dynamic Differentiated Product Oligopoly," Research Reports 149978, University of Connecticut, Food Marketing Policy Center.
  9. Ren, Bijie & Polasky, Stephen, 2014. "The optimal management of renewable resources under the risk of potential regime shift," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 195-212.

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