Applied Computational Economics and Finance [Hardcover]

Mario J. Miranda (Author), Paul L. Fackler (Author)

Book Description

ISBN-10: 0262134209 | ISBN-13: 978-0262134200 | Publication Date: September 16, 2002

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.

Editorial Reviews

Review

"One of this book's many strengths is its structure, the way theory-based chapters alternate with analytical ones. This will make it an invaluable resource in the classroom."--Thomas J. Sargent, Hoover Institution, Stanford UniversityPlease note: Note slight change to quote and to institutional affiliation. Apologies for the many recirculations. Thanks!

About the Author

Paul L. Fackler is Associate Professor, Department of Agricultural and Resource Economics, North Carolina State University.

Mario J. Miranda is Professor and Chair of Graduate Studies, Department of Agricultural, Environmental, and Development Economics, Ohio State University.

Product Details

• Hardcover: 512 pages
• Publisher: The MIT Press (September 16, 2002)
• Language: English
• ISBN-10: 0262134209
• ISBN-13: 978-0262134200
• Product Dimensions: 9.5 x 7 x 1 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 4.4 out of 5 stars  See all reviews (9 customer reviews)
• Amazon Best Sellers Rank: #636,456 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

11 of 13 people found the following review helpful:

5.0 out of 5 stars Fantastic book!, September 17, 2003

By 

"feersum_monkey" (Princeton, NJ United States) - See all my reviews

This review is from: Applied Computational Economics and Finance (Hardcover)

This book is a must for economists or financial engineers. I used the book in a course and refer to it often now that I work in the industry. The Matlab examples are also excellent.

10 of 12 people found the following review helpful:

5.0 out of 5 stars Excellent for economists and financial analysts, July 16, 2004

By 

Patrick L. Anderson (Michigan USA) - See all my reviews
(REAL NAME)   

This review is from: Applied Computational Economics and Finance (Hardcover)

This is one of the few books that covers the topics of numerical methods to solve finance and economics problems. It provides a large number of generic applications.

Readers that can use Matlab will especially benefit. If so, be sure to get the author's toolbox and see the errata on the author's page.

There are two other books that might be useful to those interested in this text: Dixit and Pindyck's Investment Under Uncertainty (1994), and Patrick Anderson's Business Economics and Finance (2004) [my book], which cites the Dixit and Miranda texts.

Readers should be prepared for some math, although it is much more accessible here than in most graduate texts in financial mathematics.

2 of 2 people found the following review helpful:

2.0 out of 5 stars Worth adding to your library, but nothing new, and much misleading, May 7, 2011

By 

Herbert Gintis (Northampton, MA USA) - See all my reviews

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This review is from: Applied Computational Economics and Finance (Hardcover)

It is very common in the natural sciences to have exact analytical results, but to lack the mathematical techniques to provide analytical solutions to the resulting equations. In many cases this is due to incomplete knowledge, so some future mathematician will come up with solutions that do not now exist. However, it is often the case that there do not exists closed-form solutions, or the problem is so large that the required calculations are infeasible. The latter is often the case with so-called complex systems---they are complex only in the sense that the solution space exceeds our capacity to calculate.

In such situations, the accepted research technique is to find approximate solutions for an appropriate range of model parameters. This book is devoted to providing techniques for this "computational" approach. The authors' preferred models are dynamic optimization models, and somewhat ironically, their presentation of the models (although not going beyond Bellman and Lucas-Stokey) is the more interesting part of the book. By contrast, their presentation of computational methods is elementary, basically describing a tool kit using the MatLab software environment. This is a serious error, because it leads the user away from useful computational techniques.

The book opens with techniques for solving linear equations and approximating roots to continuous functions. In fact, the user rarely needs to know such details, but rather should go to Mathematica or Maple software that can do a better job in 99% of the cases that a casual user will ever do knowing the few classical techniques used in this book. But the authors never mention any software except MatLab, which is good for some things but not very good for others.

The biggest gap in the books is its treatment of Markov processes, which are ubiquitous in models of choice and strategic interaction. Markov processes are classic examples of analytical models in which it is easy to write down the equilibrium and even the dynamics, but the equations are many orders of magnitude too numerous to solve in human dimensions of time and space. Moreover, in my estimation Markov models are much more important than dynamic optimization models, which presume much more information on the part of the decision-maker than is usually available (outside of an engineering context). Finanical economic is a mess in part because it makes assumptions that allow dynamic optimization to appear to provide useful solutions, but in fact more realistic behavioral models, taking seriously the information possessed by decision-makers, would be much more useful.

As an alternative to this book, I would look at Judd and Tesfatsion's Handbook of Computational Economic and the many references therein.