Book Description
The explosive growth in computational power over the past several decades offers new tools and opportunities for economists. This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for "agents" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; innovation and technological change; organizations; market design; automated markets and trading agents; political economy; social-ecological systems; computational laboratory development; and general methodological issues.
*Every volume contains contributions from leading researchers
*Each Handbook presents an accurate, self-contained survey of a particular topic
*The series provides comprehensive and accessible surveys
Customer Reviews:
An Invaluable Resource for Practicing and Novice Agent-based Modelers.......2006-12-29
This excellent volume should be entitled "Explorations in Agent-Based Modeling," as a comparison with Volume I of the Handbook of Computational Economics should make clear. The earlier volume is an extremely mature product summarizing the application of computer-intensive mathematical techniques to traditional economic problems--a subject the history of which goes back to the earliest applications of computers during World War II. The volume under review, Volume II, has a completely different character. Agent-based modeling is a young and vigorous, rather than a mature and technically plodding science. Mathematics, rather than being the central focus, tends to be rather a simple-minded tool, and the programming, rather than being of the number-crunching variety, tends to be a versatile and imaginative mirroring of real-world processes in silicon life-forms and object-oriented structures. The subject matter, moreover, is not limited to the bread and butter of traditional economics (computable general equilibrium, solving for Nash equilibria, macroeconomic modeling, parallel computation, dynamic programming, and the like), but rather explores novel themes in the interface between economics and the other behavioral sciences--especially in this volume politics, biology, and ecology. The chapters do accomplish fairly comprehensive literature reviews (but beware--in this fast-moving field some of the most important contributions are likely to be the most recent, and hence not referenced), but they are rarely technically detailed summaries of the state-of-the-art. Rather, chapters tend to develop themes that are particularly interesting to the author. This makes for a very readable volume, but I am not sure the appellation "Handbook" is truly appropriate.
Tesfatsion's first sentence in her introductory essay to the volume gets right to the point. "Economies," she asserts, "are complex dynamic systems." What, we may ask, makes an economy a complex dynamic system? For one thing, the complex economy is never in equilibrium, but is constantly subjected to shocks, both exogenous and endogenous, that affect its short-term movements. There are frequent local nonlinear resonances that lead to significant deviations of economic variables (prices, quantities, wages, asset prices) from their equilibrium values even in the absence of strong or systematic perturbations to the system. We see such deviations in many economic time series, which often have the "fat tails" characteristics of the power laws of complex systems, as opposed to the Gaussian distributions of Neoclassical theory. Second, in a complex (a.k.a. real-world) economy, the Law of One Price fails. For instance, in the European Union, the standard deviation of prices rose from 12.3% in 1998 to 13.8% in 2003, despite the extensive dropping of trade barriers and movement to a common currency over this period. A third characteristic of the complex economy is that it rarely, if ever, achieves the sort of optimality that can be attained in simple engineered systems. For instance, since economies are rarely in equilibrium, most production, trade, and consumption takes place out of equilibrium, and hence is Pareto-suboptimal, at least when measured against a complete information Walrasian economy that has somehow attained equilibrium.
It is evident, then, that standard Neoclassical economic theory, as taught in the college and graduate textbooks and developed in the mainstream economics journals, does not recognize that the economy is a complex dynamic system. If the first volume of this pair of Handbooks might be called "how to do traditional economics better with computers," the volume under consideration could be called "How to transform economic theory using agent based modeling." We can chart the following characteristics of the complex economy: (a) The complex economy is thermodynamically open, dynamic, nonlinear, and generally far from equilibrium, whereas the Walrasian economy is thermodynamically closed, static, and linear in the sense that it can be understood using algebraic geometry and manifold theory; (b) In the complex economy, agents have limited information and face high costs of information processing. However, under appropriate conditions, they evolve non-optimal but highly effective heuristics for operating in complex environments. There is no assurance that when faced with novel environments, individuals will shift efficiently to new heuristics. In the Neoclassical economy, by contrast, agents have perfect information and can costlessly optimize; (c) Agents in the complex economy participate in sophisticated overlapping networks that allow them to compensate for having limited information and facing formidable information processing costs. In the Walrasian economy, agents do not interact at all. Rather, each agent faces an impersonal price structure; (d) In the complex economy, macroeconomic patterns are emergent properties of micro-level interactions and behaviors, in the same sense as the chemical properties of a complex molecule, such as carbon, is an emergent property of its nuclear and electronic structure, or that thermodynamics is an emergent property of many-particle systems. In such cases we cannot analytically derive the properties of the macro system from those of its component parts, although we can apply novel mathematical techniques to model the behavior of the emergent properties. In the case of the complex economy, these higher level modeling constructs are currently largely absent, although agent-based modeling may provide the data needed to develop the appropriate mathematical tools. By contrast, the Walrasian economy has no macro properties that cannot be derived from its micro properties (for instance, the First and Second Welfare Theorems); (e) In the complex economy, the evolutionary process of differentiation, selection, and amplification provides the system with novelty and is responsible for the growth in order and complexity. In the Walrasian economy there is no mechanism for creating novelty or growth in complexity. In his chapter in this book, Axel Leijonhufvud develops the insight that many contributions to economic theory from the Marshallian tradition, effectively eclipsed by the influence of Edgeworth, Walras, and their general equilibrium successors, are echoed and developed in the agent-based simulations of economic dynamics.
Several authors address the question as to the epistemological status of agent-based models. It is indicative of the youth of this brand of research that widely divergent answers are offered. One such view is that agent-based modeling is an alternative to formal analytical economic theory. It strikes me that this is not at all the case. Rather, an agent-based model is a set of empirical data, and building such models is akin to laboratory experimentation. One can use the results of such experimentation to inspire theorists to construct analytical models in which one can derive logically the properties of the system observed in the laboratory. Or, if the complexity of the system precludes analytical modeling, one can make broad generalizations based on a comparative study of different agent-based systems. In principle, an agent-based model could provide an existence theorem for a particular emergent phenomenon, but in general there are sufficient differences between a mathematical model of a process and its agent-based implementation (for instance, real numbers are approximated by fixed-precision floating point numbers, and random numbers are approximated by deterministic algorithms with long periods), that the two models could have substantively different properties.
Representing ABM models as empirical rather than theoretical contributions is likely to improve the chances for publication in mainstream journals, and hence improve the communication among economists. Economic theorists often make the point to me that in reading an analytical paper, the assumptions and the method of proof are completely transparent, while an agent-based model must be taken on faith, since the model itself is not presented in a journal article, nor would it make much sense if it were, except to an expert in the computer language used. If the ABM is presented as a contribution to theory, it is easy to see why it is rejected by respectable journals: it is asking the reader to take the authors' assertions on faith alone. If the ABM results are represented as empirical data, this problem disappears.
When agent-based models are not accepted in mainstream economics journals, modelers tend to place the blame on the closed-mindedness and traditionalistic mentality of the reviewers. I consider this a very serious error, because it gives the agent-based modeler no means of correcting the problem. I think that it is almost always good advice to blame yourself when a paper is rejected, because the you is the only one with an incentive to change to meet the reviewers' criteria the next time around. The authors in this volume do not make this mistake, and several have valuable suggestions as to how agent-based models must be crafted to increase their scientific value (Robert Axelrod's suggestions are particularly incisive).
It is interesting that none of the authors appears to have noticed the inverse problem: agent-based models are all the rage in some circles, and many faulty models get past reviewers and are published in top journals, including Science and Nature. The fact is that if two researchers are given the same specifications and write the computer code independently, there is a very good chance their models will differ in substantial ways. There is simply no way for a reviewer to assess the quality of a simulation without spending a considerable amount of time going over the code. Moreover, I have found that researchers often bias code generation in such a way as to support their pet theories. The nature of this bias often cannot be revealed without a thorough inspection of the computer code. This sort of author behavior is not not necessarily due to our dishonesty, but rather due to our capacity to self-delude. If the ABM behaves the way we want it to, we leave the code alone. If it does not, we work over it to find out why. The resulting code is thus virtually certain to be self-serving and biased.
I do not know how to get around this problem. It is reminiscent of a similar problem with econometric research with complex data sets, where it is virtually impossible for reviewers to ascertain the significance of the results, especially in the case of economic time series. In the case of econometric analysis, the problem is attenuated if researchers are obligated to place the data in the public domain, making replication feasible. In the case of agent-based models, there is usually no "data" different from the model itself. It would be a step forward to require researchers to place their code in the public domain, so that the threat of public scrutiny might serve to attenuate the temptation to torture the code whose results one does not like, while coddling the code that reinforces our prejudices and expectations.
Another important issue not systematically addressed in this Handbook is the mechanics of producing an agent-based model. If the researcher does not do his or her own programming, clearly the researcher should generated a completely unambiguous set of specifications for programming the model. However, if the research does not know computer programming, this is impossible in all but the most simple cases. Even if the researcher is an expert programmer, he or she cannot pre-envision exactly how the model should function, since often one tries several alteratives for each piece of code, and one often does not know what the real dynamics of the model are until one has done considerable hands-on programming. For this reason, if I had my way, I would never accept a paper for publication that was not programmed by the researchers themselves, except for the simplest sort of models. Therefore, I believe training in ABM should include training in computer programming to the point of professional proficiency. I do not even accept using canned ABM software, because it is difficult to tell what the software is doing, the implementation is always painfully slow compared to a real computer language, and there are strict limits as to what can be accomplished with such software. However, I know that many leading ABM researchers disagree with this, and happily teach their students to use Swarm, StarLogo, and the like. Until this issue is thoroughly investigated and the truth sorted out from the myth, ABM will remain of limited value to the economic research community.
I commend the Editors for doing a fine job in addressing the needs of the ABM community, while producing a volume that can be profitably read by those new to the field. Nevertheless, there remain hard problems that must be soberly addressed before ABM becomes a standard part of the repertoire of economic researchers, and ABM results appear widely in top economics journals.
Book Description
Modern business cycle theory and growth theory uses stochastic dynamic general equilibrium models. Many mathematical tools are needed to solve these models. The book presents various methods for computing the dynamics of general equilibrium models. In part I, the representative-agent stochastic growth model is solved with the help of value function iteration, linear and linear quadratic approximation methods, parameterised expectations and projection methods. In order to apply these methods, fundamentals from numerical analysis are reviewed in detail. Part II discusses methods for solving heterogeneous-agent economies. In such economies, the distribution of the individual state variables is endogenous. This part of the book also serves as an introduction to the modern theory of distribution economics. Applications include the dynamics of the income distribution over the business cycle or the overlapping-generations model. Through an accompanying home page to this book, computer codes to all applications can be downloaded.
Average customer rating:
- Somewhat dated...but still helpful
|
Computational Economics and Finance: Modeling and Analysis with Mathematica (Economic & Financial Modeling with Mathematica)
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
 Econometrics
| Economics
| Business & Investing
| Subjects
| Books
Theory
| Economics
| Business & Investing
| Subjects
| Books
General
| Popular Economics
| Business & Investing
| Subjects
| Books
Operations Research
| Management & Leadership
| Business & Investing
| Subjects
| Books
Graphics & Multimedia
| Programming
| Computers & Internet
| Subjects
| Books
| DirectX
| Flash
| GIS
| General
| OpenGL
| Solid Works
General
| Science
| Subjects
| Books
General
| Applied
| Mathematics
| Science
| Subjects
| Books
Discrete Mathematics
| Pure Mathematics
| Mathematics
| Science
| Subjects
| Books
General
| Applied
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
Discrete Mathematics
| Pure Mathematics
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
Software
| Information Systems
| Computer Science & Information Systems
| New & Used Textbooks
| Stores
| Books
General
| Business & Finance
| New & Used Textbooks
| Stores
| Books
History & Theory
| Economics
| Business & Finance
| New & Used Textbooks
| Stores
| Books
General
| Economics
| Business & Finance
| New & Used Textbooks
| Stores
| Books
All Amazon Upgrade
| Amazon Upgrade
| Stores
| Books
Business & Investing
| Amazon Upgrade
| Stores
| Books
Computers & Internet
| Amazon Upgrade
| Stores
| Books
Professional & Technical
| Amazon Upgrade
| Stores
| Books
Science
| Amazon Upgrade
| Stores
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Business & Investing
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Computers & Internet
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Professional
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Science
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Similar Items:
-
Mathematica for Microeconomics
-
Computational Financial Mathematics using Mathematica
-
Mathematics and Mathematica for Economists
-
Schaum's Outline of Mathematica
-
An Introduction to Programming with Mathematica, Third Edition
ASIN: 0387945180 |
Book Description
As with the first volume, Volume Two of
Economic and
Financial Modeling with Mathematica is edited by Hal Varian, and its contributors are carefully selected by him to assure a high quality, practical work reflecting the efforts and expertise of an international cadre of Mathematica users from the economic, financial, investments, quantitative business and operations research communities.
Customer Reviews:
Somewhat dated...but still helpful.......2002-01-19
For the reader well-versed in Mathematica and in economic theory, this book gives a fairly good overview of how Mathematica can be used to study mathematical economics and finance. It is also assumed in the articles in the book that the reader has a strong background in mathematics. Since the book was published in 1993, Mathematica has considerably expanded, with many new features that make some of the accompanying code in the book somewhat dated, but the notebooks can still be used beneficially.In addition, economic theory is currently making more use of symbolic programming, and financial analysis has exploded as an area which is now making heavy use of high-performance computing. Although Mathematica cannot compete from a performance standpoint with the needs of financial engineering, it still has an advantage from a didactic standpoint. I did not read all of the articles in the book, so my comments will be limited to the ones that I did.
The article on "Mathematica and Diffusions" is an overview of how to use Mathematica to do stochastic calculus. The Ito calculus is reviewed briefly, and the authors begin with constructing a Weiner process. The Mathematica package they employ and on the disk accompanying the book is not discussed in detail, but is merely used to simulate realizations of the process. Readers who want a more in-depth view will have to go over the code themselves. The authors use the package to generate realizations of Weiner processes that are correlated with each other, and show this correlation via Mathematica graphics. The Black-Scholes formula is derived using the standard self-financing trading strategy and ignoring transaction costs and dividends. The algebraic manipulations are done with Mathematica, and this obscures (a little) the underlying concepts behind the derivation of this important formula. Since data structures in Mathematica are essentially lists, the authors outline the construction of the data structure that could be used to represent a diffusion, namely a list consisting of five terms: the diffusion, Weiner process name, expression for the drift and dispersion, and the initial value. For the reader familiar with OO-programming, accessor functions are used to extract the components of this data structure. This is a nice move by the authors, for it is an example of how Mathematica can be used to emulate OO-programming.
The article "Itovsn3: Doing Stochastic Calculus with Mathematica" is an overview of how to use the Itovsn3 package that is on the disk to implement Ito calculus. It is assumed that the reader has a background in stochastic calculus, since the author does not give a review. However, semimartingales, so important to those working in financial engineering, are discussed and their statistical behavior described using Mathematica. The Ito formula is presented as a semimartingale-type decomposition for smooth function of Brownian motion and the author shows using Mathematica plots how the higher order terms in the second-order Taylor expansion vanish asymptotically. This article is not merely Mathematica code for Ito calculus, for the author gives an example of how to use the package in a hedging problem.
The article "Option Valuation" is a more detailed overview of how to use Mathematica in the context of the Black-Scholes model to perform options valuation and risk management. Heavy use is made of the graphics capability of Mathematica to illustrate how option values change as a function of stock price and time of expiration. The author also shows how Mathematica can be used as a OO-language to treat options as self-contained objects with accessor functions. He does however state that Mathematica does not live up to the OO toolkits available elsewhere, contrary to my experience. He closes the article with a consideration of how to use Mathematica to value options that can be exercised before expiry, the binomial model playing the central role in the discussion. It is here in particular that the performance of Mathematica is readily felt. The numerical number-crunching needed to do the calculations in these types of models cannot be done in Mathematica efficiently and profitably.
The article "Time Series Models and Mathematica" gives a general treatment on how Mathematica can be used to study ARIMA models for time series. Mathematica is used more interactively than the other articles and the visualization obtained is quite nice in giving the reader insight into such concepts as the moving average and the spectral density function. The author shows how to estimate the spectral density function and why periodogram techniques fall short in this estimation. I would have liked to see other techniques for studying time series discussed, such as neural networks and hidden Markov models, but the author does do a fairly good job with the ARIMA models.
Average customer rating:
|
Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics (Studies in Computational Finance, Volume 2) (Studies in Computational Finance)
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
Econometrics
| Economics
| Business & Investing
| Subjects
| Books
General
| Popular Economics
| Business & Investing
| Subjects
| Books
Finance
| Business & Investing
| Subjects
| Books
| Banks & Banking
| Corporate Finance
| Foreign Exchange
| Inflation
| Interest
General
| Business & Investing
| Subjects
| Books
General
| Accounting
| Industries & Professions
| Business & Investing
| Subjects
| Books
General
| Mathematics
| Science
| Subjects
| Books
General
| Medicine
| Subjects
| Books
General
| Accounting
| Accounting & Finance
| Professional & Technical
| Subjects
| Books
All Amazon Upgrade
| Amazon Upgrade
| Stores
| Books
Business & Investing
| Amazon Upgrade
| Stores
| Books
Medicine
| Amazon Upgrade
| Stores
| Books
Professional & Technical
| Amazon Upgrade
| Stores
| Books
Science
| Amazon Upgrade
| Stores
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Business & Investing
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Medicine
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Professional
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Science
| Qualifying Textbooks - Fall 2007
| Stores
| Books
ASIN: 0792376803 |
Book Description
Over the last decade, dynamical systems theory and related nonlinear methods have had a major impact on the analysis of time series data from complex systems. Recent developments in mathematical methods of state-space reconstruction, time-delay embedding, and surrogate data analysis, coupled with readily accessible and powerful computational facilities used in gathering and processing massive quantities of high-frequency data, have provided theorists and practitioners unparalleled opportunities for exploratory data analysis, modelling, forecasting, and control.
Until now, research exploring the application of nonlinear dynamics and associated algorithms to the study of economies and markets as complex systems is sparse and fragmentary at best.
Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters.
Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.
Average customer rating:
|
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Stefan Voß , and
David L. Woodruff
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
Econometrics
| Economics
| Business & Investing
| Subjects
| Books
General
| Popular Economics
| Business & Investing
| Subjects
| Books
General
| Business & Investing
| Subjects
| Books
Management Science
| Management & Leadership
| Business & Investing
| Subjects
| Books
Operations Research
| Management & Leadership
| Business & Investing
| Subjects
| Books
Quality Control
| Management & Leadership
| Business & Investing
| Subjects
| Books
General
| Applied
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
Information Systems
| Software Engineering
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Computers & Internet
| Subjects
| Books
All Amazon Upgrade
| Amazon Upgrade
| Stores
| Books
Business & Investing
| Amazon Upgrade
| Stores
| Books
Computers & Internet
| Amazon Upgrade
| Stores
| Books
Professional & Technical
| Amazon Upgrade
| Stores
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Business & Investing
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Computers & Internet
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Professional
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Similar Items:
-
Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies
ASIN: 3540298789 |
Book Description
The book begins with an easy-to-read introduction to the concepts associated with the creation of optimization models for production planning. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation fairly sophisticated models for supply chain management are developed. Another unique feature is that models are developed with an eye toward implementation. In fact, there is a chapter that provides explicit examples of implementation of the basic models using a variety of popular, commercially available modeling languages. The new edition is updated and provides extensions.
Book Description
Researchers are increasingly turning to computational methods to study the dynamic properties of political and economic systems. Politicians, citizens, interest groups, and organizations interact in dynamic, complex environments, and the static models that are predominant in political economy are limited in capturing fundamental features of economic decision making in modern democracies. Computational models--numerical approximations of equilibria and dynamics that cannot be solved analytically--provide useful insight into the behavior of economic agents and the aggregate properties of political systems. They serve as a valuable complement to existing mathematical tools.
This book offers some of the latest research on computational political economy. The focus is on theoretical models of traditional problems in the field. Each chapter presents an innovative model of interaction between economic agents. Topics include voting behavior, candidate position taking, special interest group contributions, macroeconomic policy making, and corporate decision making.
Book 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.
Customer Reviews:
Applied Computation Economics and Finance.......2007-10-13
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've looked elsewhere and...others are worse.......2007-10-06
I bought this book b/c its required for a course I'm taking; my professor is one of the authors. Coding is a pain regardless of how good your instruction is, so I'm hesitant to criticize the book. That said, I didn't love it. However, I've looked at other books and this is by far the most relevant for using MATLAB for econ and finance.
Good but ..........2006-03-26
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.
Excellent for economists and financial analysts.......2004-07-16
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.
Fantastic book!.......2003-09-17
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.
Book Description
Economics doesn't have to be a mystery anymore. FOUNDATIONS OF MATHEMATICAL AND COMPUTATION ECONOMICS shows you how mathematics impacts economics and econometrics using easy-to-understand language and plenty of examples. Plus, it goes in-depth into computation and computational economics so you'll know how to handle those situations in your first economics job. Get ready for both the test and the workforce with this economics textbook.
Customer Reviews:
Good introduction.......2007-08-20
There is little doubt that econometrics is becoming more intensely mathematical with each passing year, making considerable demands on the intellectual capabilities of economists and also exploiting the computational powers of the latest computing machinery. Therefore, individuals who have ambitions to become involved in econometrics must begin mathematical preparation early in their education. This book fills the need for such individuals, and is written for the advanced undergraduate student in economics. It covers elementary calculus, including multivariable calculus, linear algebra, dynamic optimization, elementary numerical analysis, and a brief introduction to the theory of dynamical systems. More esoteric topics such as game theory, fixed-point analysis, and partial differential equations are not discussed in the book.
Some of the more interesting or useful features of the book include:
· The use of Matlab as a tool for calculating quantities of interest, such as the diagonalization of matrices and the numerical solution of differential equations, and its ability to access data on Excel spreadsheets. SAS has been the niche language for many in economics and econometrics, but this has been changing in recent years due to the availability of relatively inexpensive but professional symbolic programming packages like Mathematica, Matlab, and Maple. The reader who is charged with doing econometric analysis on very large data sets, with possibly millions of rows will of course not be able to implement them in Excel due to its row limitation size, vitiating the use of Matlab for such analysis.
· The inclusion of various economic concepts and models in the text, particularly in the exercises, in order to continually reinforce the idea that the book is written for economists.
· The discussion early on in the book on the philosophy of mathematics, to dissuade the skeptical reader as to the value of learning the mathematics for use in econometrics. Interestingly, the author points to the use of mathematics by none other than Karl Marx. It would be interesting to contemplate what Marx would have thought about the current state of econometrics, as heavily mathematical as it is. Along these same lines, the field of labor economics, which Marx could indeed be classified under, is currently one that makes heavy use of quantitative analysis.
· The discussion on the calibration of quantitative models in econometrics. The author points to the origin of calibration in physics, but emphasizes its necessity in the validation of models. However, and somewhat disappointingly, he does not elaborate on calibration in the book, which is unfortunate since in practice, particularly in the financial industry, calibration is extremely important and has become almost a field unto itself.
Average customer rating:
|
Barriers and Bounds to Rationality
Peter Albin
Manufacturer: Princeton University Press
ProductGroup: Book
Binding: Hardcover
Econometrics
| Economics
| Business & Investing
| Subjects
| Books
Theory
| Economics
| Business & Investing
| Subjects
| Books
General
| Popular Economics
| Business & Investing
| Subjects
| Books
General
| Business & Investing
| Subjects
| Books
Research
| Social Sciences
| Nonfiction
| Subjects
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Business & Investing
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Nonfiction
| Qualifying Textbooks - Fall 2007
| Stores
| Books
ASIN: 0691026769 |
Book Description
Peter Albin is known for his seminal work in applying the concepts of adaptive dynamical systems, first developed by biologists and physicists, to the study of economic systems. This book is a collection of his pathbreaking articles on the application of cellular automata and complexity theory to economic problems. Duncan Foley provides a thoughtful introduction in which he reviews the disparate analytical sources of Albin's work in the theories of nonlinear dynamical systems, economic dynamics, cellular automata, linguistic and computational complexity, and bounded rationality.
Albin has analyzed economic systems as interactions of highly complex components (i.e., intelligent human beings). He uses the theories of generative linguistics and cellular automata to establish that the complexity level of economic systems is, in principle at least, that of a Turing machine or general-purpose computer, establishing that classic economic approaches to the problems of household and firm choice, macroeconomic prediction, and policy evaluation may give rise to undecidable propositions and uncomputable functions. He develops simple models of dynamic economic interaction based on cellular automata which illustrate the inherent complexity of economic interactions and the resulting challenge they pose to traditional theories of rational economic behavior. These models explore the dynamics of the business cycle, decentralized market trading, and the emergence of cooperation in a novel local-interaction version of the repeated prisoners' dilemma game. Albin's work provides a unique and important perspective on economic systems.
Average customer rating:
|
Optimisation, Econometric and Financial Analysis (Advances in Computational Management Science)
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
Econometrics
| Economics
| Business & Investing
| Subjects
| Books
General
| Business & Investing
| Subjects
| Books
Banks & Banking
| Industries & Professions
| Business & Investing
| Subjects
| Books
Manager's Guides to Computing
| Business & Culture
| Computers & Internet
| Subjects
| Books
Heuristic & Constrained Search
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Computers & Internet
| Subjects
| Books
Linear Programming
| Applied
| Mathematics
| Science
| Subjects
| Books
Linear Programming
| Applied
| Mathematics
| Professional Science
| Professional & Technical
| Subjects
| Books
General
| Finance
| Accounting & Finance
| Professional & Technical
| Subjects
| Books
ASIN: 3540366253 |
Books:
- Hardwiring Excellence: Purpose, Worthwhile Work, Making a Difference
- History: Fiction or Science? (Chronology, No. 1)
- History: Fiction or Science? (Chronology, No. 1)
- History: Fiction or Science? (Chronology, No. 1)
- History: Fiction or Science? (Chronology, No. 1)
- History: Fiction or Science? (Chronology, No. 1)
- History: Fiction or Science? (Chronology, No. 1)
- How Markets Really Work: A Quantitative Guide to Stock Market Behavior
- How to Measure Training Results : A Practical Guide to Tracking the Six Key Indicators
- Infinite Dimensional Analysis: A Hitchhiker's Guide
Books Index
Books Home
Recommended Books
- Brother One Cell: An American Coming of Age in South Korea's Prisons
- The Betrothed of Death: The Spanish Foreign Legion During the Rif Rebellion, 1920-1927
- Entourage: A Tracing File for Architects and Interior Design
- Introduction to the General Principles of Aquaculture
- History: Fiction or Science
- Reading Financial Reports For Dummies
- Scratch the Surface
- Decorating With Concrete Indoors: Fireplaces, Floors, Countertops, & More
- Front & Backyard Idea Book Collection: Entries Paths & Steps Play Spaces Foundation Planting
- Liverworts and Hornworts of Southern Michigan
Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity)
Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)
Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation
Handbook of Computational Economics
Agent-Based Computational Modelling: Applications in Demography, Social, Economic and Environmental Sciences (Contributions to Economics)
