Applied Computational Economics and Finance (The MIT Press) by Mario J. Miranda (PDF)

Description

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.

User’s Reviews

Editorial Reviews: Review This book is an important contribution to the rapidly growing literature on computational economics and finance. It provides an extremely well-integrated presentation of dynamic economic models and some of the most effective numerical methods for solving them. It reinforces these ideas by providing illustrative solutions written in Matlab. This book should enable most people who do not have extensive prior background in computation to understand the key methods and ideas, and to actually begin applying these methods to their own problems. I think it will be an essential part of the toolkit of the applied practitioner in economics or finance.―John Rust, Professor of Economics, University of Maryland About the Author Mario J. Miranda is Professor and Chair of Graduate Studies, Department of Agricultural, Environmental, and Development Economics, Ohio State University.Paul L. Fackler is Associate Professor, Department of Agricultural and Resource Economics, North Carolina State University.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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.

⭐I was looking for a book that teaches how to use MATLAB to solve certain finance and economics problems, and purchased this book. The book covers very interesting topics and discusses many types of solution methods. However, the applications to MATLAB are not presented in a user-friendly way. In particular, they do not present things in a step-by-step manner and assume many things. The reader is then left to figure out how to complete programs either from some other part of the book or from prior knowledge. Thus, the book is successful in letting the reader become aware of the capabilities of MATLAB (i.e. what sort of computational techniques the program can do). However, it would havae been best if the authors wrote all the programs with complete codes. They often mention that the code assumes that the reader does this and does that.

⭐What I like about this book is that it combines theory, with Matlab code to practice. Besides, the theory of numerical analysis is treated in a very focused way, so you won’t be learning very abstract stuff, instead, you will learn the basic theory needed to understand how numerical analysis helps in economics and finance. I took the course given by the author Mario Miranda, that also helped a lot. The CompEcon toolbox for Matlab is EXTREMELY useful.

⭐I used this book for a course taught in my Graduate studies. This book makes understanding concepts so much easy, and is very readable as well. It provides examples with codes written to carry out the examples as well. The end-of-chapter exercises are also helpful and relevant. A must purchase for any economics students interested in Computational and neumerical economics.

⭐very good book for applied economics with many examples and usefull Matlab codes. Very good and useful Matlab toolkit.However, the theoretical side is relatively weak and not covered well.

⭐This is a really good book in numerical methods. It goes step by step and has exercises you can do while reading the book that help you not only understand the topics and do it yourself, but apply numerical methods to every-day problems.

⭐Excellent

⭐The book is a very good introduction to numerical methods in economics. You won’t learn MATLAB or numerical methods there, but you’ll learn how to use that in econômica.

⭐va bene nei dettagli e copre un buona parte delle teorie economiche e finanziare.Lascia a desiderare la strutturazione un po scarna.Ma resta comunque un buon libro.

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⭐Ce livre interesse principalement les economistes niveau PhD qui veulent resoudres des problemes nonlineaires. Le benchmark dans les bouquins sur méthodes numériques pour economistes est Judd. Donc, par rapport à Judd le livre est:- inférieure à Judd dans son traitement textbook des méthodes numériques pour économistes- de didactique plus simple que Judd- avec applications plus limitées mais séléctionnées intéligemment par rapport à Judd. Les auteurs selectionnent directement les méthodes qu’en pratique vous allez utiliser dans la forte majorité des cas (p.ex. polynômes de Chebyshev colloqués ou splines quand on parle de projections, les autres options ne sont mêmes pas mentionnées).- gros avantage dans son associations à des codes en Matlab. Ces codes sont fortement optimisés (codes satellites en C) et donc très efficients en termes de temps.

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⭐Computational economicsの教科書としてはJuddが有名ですが、本書はよりHands-onな感じで、「体で覚える」タイプの人には最適だと思います。また、産業組織論などで多用されている動学的ゲームの数値的解法を習得するには、本書のほうが向いていると思います。

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