Customer Reviews

Applied Computational Economics and Finance

5 star:		(6)
4 star:		(2)
3 star:		(0)
2 star:		(1)
1 star:		(0)

 

 

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.

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.

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.

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.

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.

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.

In Economics I often find it difficult to write the codes that replicate the models in many papers. This book is an excellent introduction to computational economics and for actually solving things in Matlab.

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.