HOME

TheInfoList



OR:

Computational Economics is an interdisciplinary research discipline that involves
computer science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...
,
economics Economics () is the social science that studies the production, distribution, and consumption of goods and services. Economics focuses on the behaviour and interactions of economic agents and how economies work. Microeconomics anal ...
, and management science.''Computational Economics''.
"About This Journal"
an
"Aims and Scope
"
This subject encompasses
computational modeling Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be dete ...
of economic systems. Some of these areas are unique, while others established areas of economics by allowing robust data analytics and solutions of problems that would be arduous to research without computers and associated numerical methods.• Hans M. Amman, David A. Kendrick, and John Rust, ed., 1996. ''Handbook of Computational Economics'', v. 1, Elsevier
Description
& chapter-previe
links.
   •
Kenneth L. Judd Kenneth Lewis Judd (born March 24, 1953) is a computational economist at Stanford University, where he is the Paul H. Bauer Senior Fellow at the Hoover Institution. He received his PhD in economics from the University of Wisconsin in 1980. He ...
, 1998. ''Numerical Methods in Economics'', MIT Press. Links t
description
an
chapter previews
Computational methods have been applied in various fields of economics research, including but not limiting to:   
Econometrics Econometrics is the application of statistical methods to economic data in order to give empirical content to economic relationships. M. Hashem Pesaran (1987). "Econometrics," '' The New Palgrave: A Dictionary of Economics'', v. 2, p. 8 p. ...
: Non-parametric approaches, Semi-parametric approaches, and
Machine Learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
. Dynamic Systems Modeling: Optimization, Dynamic stochastic general equilibrium modeling, and
Agent-based modeling An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and wha ...
.Scott E. Page, 2008. "agent-based models," ''
The New Palgrave Dictionary of Economics ''The New Palgrave Dictionary of Economics'' (2018), 3rd ed., is a twenty-volume reference work on economics published by Palgrave Macmillan. It contains around 3,000 entries, including many classic essays from the original Inglis Palgrave Dictio ...
'', 2nd Edition
Abstract


History

Computational economics developed concurrently with the mathematization of the field. During the early 20 century, pioneers such as Jan Tinbergen and
Ragnar Frisch Ragnar Anton Kittil Frisch (3 March 1895 – 31 January 1973) was an influential Norwegian economist known for being one of the major contributors to establishing economics as a quantitative and statistically informed science in the early 20th c ...
advanced the computerization of economics and the growth of econometrics. As a result of advancements in Econometrics, regression models,
hypothesis testing A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. ...
, and other computational statistical methods became widely adopted in economic research. On the theoretical front, complex
macroeconomic Macroeconomics (from the Greek prefix ''makro-'' meaning "large" + ''economics'') is a branch of economics dealing with performance, structure, behavior, and decision-making of an economy as a whole. For example, using interest rates, taxes, an ...
models, including the
Real Business Cycle Real business-cycle theory (RBC theory) is a class of new classical macroeconomics models in which business-cycle fluctuations are accounted for by real (in contrast to nominal) shocks. Unlike other leading theories of the business cycle, RBC t ...
(RBC) model and
Dynamic Stochastic General Equilibrium Dynamic stochastic general equilibrium modeling (abbreviated as DSGE, or DGE, or sometimes SDGE) is a macroeconomic method which is often employed by monetary and fiscal authorities for policy analysis, explaining historical time-series data, as w ...
(DSGE) models have propelled the development and application of numerical solution methods that rely heavily on computation. In the 21st century, the development of computational algorithms created new means for computational methods to interact with economic research. Innovative approaches such as machine learning models and agent-based modeling have been actively explored in different areas of economic research, offering economists an expanded toolkit that frequently differs in character from traditional methods.  


Applications


Agent based modelling

Computational economics uses computer-based economic modeling to solve analytically and statistically formulated economic problems. A research program, to that end, is
agent-based computational economics Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. I ...
(ACE), the computational study of economic processes, including whole economies, as dynamic systems of interacting agents.• Scott E. Page, 2008. "agent-based models," ''The New Palgrave Dictionary of Economics'', 2nd Edition
Abstract
   • Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, ''Handbook of Computational Economics'', v. 2,
p. 831-880 P. is an abbreviation or acronym that may refer to: * Page (paper), where the abbreviation comes from Latin ''pagina'' * Paris Herbarium, at the '' Muséum national d'histoire naturelle'' * ''Pani'' (Polish), translating as Mrs. * The ''Pacific Re ...
.    • Kenneth L. Judd, 2006. "Computationally Intensive Analyses in Economics," ''Handbook of Computational Economics'', v. 2, ch. 17, pp
881-
893. Pre-pu
PDF
   • L. Tesfatsion and K. Judd, ed., 2006. ''Handbook of Computational Economics'', v. 2, ''Agent-Based Computational Economics'', Elsevier
Description
& and chapter-previe
links
   •
Thomas J. Sargent Thomas John Sargent (born July 19, 1943) is an American economist and the W.R. Berkley Professor of Economics and Business at New York University. He specializes in the fields of macroeconomics, monetary economics, and time series econometric ...
, 1994. ''Bounded Rationality in Macroeconomics'', Oxford
Description
and chapter-preview 1st-pag
links.
/ref> As such, it is an economic adaptation of the
complex adaptive system A complex adaptive system is a system that is '' complex'' in that it is a dynamic network of interactions, but the behavior of the ensemble may not be predictable according to the behavior of the components. It is '' adaptive'' in that the indiv ...
s
paradigm In science and philosophy, a paradigm () is a distinct set of concepts or thought patterns, including theories, research methods, postulates, and standards for what constitute legitimate contributions to a field. Etymology ''Paradigm'' comes f ...
.W. Brian Arthur, 1994. "Inductive Reasoning and Bounded Rationality," ''American Economic Review'', 84(2), pp
406-411
.    • Leigh Tesfatsion, 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," ''Information Sciences'', 149(4), pp
262-268
.    • _____, 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," ''Artificial Life'', 8(1), pp.55-82
Abstract
and pre-pu
PDF
.
Here the "agent" refers to "computational objects modeled as interacting according to rules," not real people. Agents can represent social, biological, and/or physical entities. The theoretical assumption of
mathematical optimization Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
by agents in equilibrium is replaced by the less restrictive postulate of agents with
bounded rationality Bounded rationality is the idea that rationality is limited when individuals make decisions, and under these limitations, rational individuals will select a decision that is satisfactory rather than optimal. Limitations include the difficulty o ...
''adapting'' to market forces,• W. Brian Arthur, 1994. "Inductive Reasoning and Bounded Rationality," ''American Economic Review'', 84(2), pp
406-411
.    •
John H. Holland John Henry Holland (February 2, 1929 – August 9, 2015) was an American scientist and Professor of psychology and Professor of electrical engineering and computer science at the University of Michigan, Ann Arbor. He was a pioneer in what became ...
and John H. Miller (1991). "Artificial Adaptive Agents in Economic Theory," ''American Economic Review'', 81(2), pp
365-370
.    •
Thomas C. Schelling Thomas Crombie Schelling (April 14, 1921 – December 13, 2016) was an American economist and professor of foreign policy, national security, nuclear strategy, and arms control at the School of Public Policy at University of Maryland, College P ...
, 1978 006 ''Micromotives and Macrobehavior'', Norton
Description
,
preview
   •
Thomas J. Sargent Thomas John Sargent (born July 19, 1943) is an American economist and the W.R. Berkley Professor of Economics and Business at New York University. He specializes in the fields of macroeconomics, monetary economics, and time series econometric ...
, 1994. ''Bounded Rationality in Macroeconomics'', Oxford
Description
and chapter-preview 1st-pag
links.
/ref> including game-theoretical contexts.Joseph Y. Halpern, 2008. "computer science and game theory," ''The New Palgrave Dictionary of Economics'', 2nd Edition.
Abstract
   • Yoav Shoham, 2008. "Computer Science and Game Theory," ''Communications of the ACM'', 51(8), pp
75-79
.    • Alvin E. Roth, 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," ''Econometrica'', 70(4), pp
1341–1378
.
Starting from initial conditions determined by the modeler, an ACE model develops forward through time driven solely by agent interactions. The scientific objective of the method is to test theoretical findings against real-world data in ways that permit empirically supported theories to cumulate over time.Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, ''Handbook of Computational Economics'', v. 2, sect. 5, p. 865
p. 831-880 P. is an abbreviation or acronym that may refer to: * Page (paper), where the abbreviation comes from Latin ''pagina'' * Paris Herbarium, at the '' Muséum national d'histoire naturelle'' * ''Pani'' (Polish), translating as Mrs. * The ''Pacific Re ...
.


Machine learning in computational economics

Machine learning models present a method to resolve vast, complex, unstructured data sets. Various machine learning methods such as the kernel method and
random forest Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of ...
have been developed and utilized in data-mining and statistical analysis. These models provide superior classification, predictive capabilities, flexibility compared to traditional statistical models, such as that of the
STAR A star is an astronomical object comprising a luminous spheroid of plasma (physics), plasma held together by its gravity. The List of nearest stars and brown dwarfs, nearest star to Earth is the Sun. Many other stars are visible to the naked ...
method. Other methods, such as causal machine learning and causal tree, provide distinct advantages, including inference testing. There are notable advantages and disadvantages of utilizing machine learning tools in economic research. In economics, a model is selected and analyzed at once. The economic research would select a model based on principle, then test/analyze the model with data, followed by cross-validation with other models. On the other hand, machine learning models have built in "tuning" effects. As the model conducts empirical analysis, it cross-validates, estimates, and compares various models concurrently. This process may yield more robust estimates than those of the traditional ones. Traditional economics partially normalize the data based on existing principles, while machine learning presents a more positive/empirical approach to model fitting. Although Machine Learning excels at classification, predication and evaluating goodness of fit, many models lack the capacity for statistical inference, which are of greater interest to economic researchers. Machine learning models' limitations means that economists utilizing machine learning would need to develop strategies for robust, statistical causal inference, a core focus of modern empirical research. For example, economics researchers might hope to identify
confounders In statistics, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Con ...
,
confidence interval In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated ''confidence level''; the 95% confidence level is most common, but other levels, such as 9 ...
s, and other parameters that are not well-specified in Machine Learning algorithms. Machine learning may effectively enable the development of more complicated
heterogeneous Homogeneity and heterogeneity are concepts often used in the sciences and statistics relating to the uniformity of a substance or organism. A material or image that is homogeneous is uniform in composition or character (i.e. color, shape, siz ...
economic models. Traditionally, heterogeneous models required extensive computational work. Since heterogeneity could be differences in tastes, beliefs, abilities, skills or constraints, optimizing a heterogeneous model is a lot more tedious than the homogeneous approach (representative agent). The development of reinforced learning and deep learning may significantly reduce the complexity of heterogeneous analysis, creating models that better reflect agents' behaviors in the economy. The adoption and implementation of
neural network A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
s,
deep learning Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. ...
in the field of computational economics may reduce the redundant work of data cleaning and data analytics, significantly lowering the time and cost of large scale data analytics and enabling researchers to collect, analyze data on a great scale. This would encourage economic researchers to explore new modeling methods. In addition, reduced emphasis on data analysis would enable researchers to focus more on subject matters such as causal inference, confounding variables, and realism of the model. Under the proper guidance, machine learning models may accelerate the process of developing accurate, applicable economics through large scale empirical data analysis and computation.  


Dynamic Stochastic General Equilibrium (DSGE) model

Dynamic modeling methods are frequently adopted in macroeconomic research to simulate economic fluctuations and test for the effects of policy changes. The DSGE one class of dynamic models relying heavily on computational techniques and solutions. DSGE models utilize micro-founded economic principles to capture characteristics of the real world economy in an environment with intertemporal uncertainty. Given their inherent complexity, DSGE models are in general analytically intractable, and are usually implemented numerically using computer software. One major advantage of DSGE models is that they facilitate the estimation of agents' dynamic choices with flexibility.  However, many scholars have criticized DSGE models for their reliance on reduced-form assumptions that are largely unrealistic.


Computational tools and programming languages

Utilizing computational tools in economic research has been the norm and foundation for a long time. Computational tools for economics include a variety of computer software that facilitate the execution of various matrix operations (e.g. matrix inversion) and the solution of  systems of linear and nonlinear equations. Various programming languages are utilized in economic research for the purpose of data analytics and modeling. Following is a typical listing of programming languages used in computational economics research: C++,
MATLAB MATLAB (an abbreviation of "MATrix LABoratory") is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementat ...
,
Julia (programming language) Julia is a high-level, dynamic programming language. Its features are well suited for numerical analysis and computational science. Distinctive aspects of Julia's design include a type system with parametric polymorphism in a dynamic progra ...
,
Python (programming language) Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected. It supports multiple programming p ...
,
R (programming language) R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinform ...
,
Stata Stata (, , alternatively , occasionally stylized as STATA) is a general-purpose statistical software package developed by StataCorp for data manipulation, visualization, statistics, and automated reporting. It is used by researchers in many fie ...
Among these programming languages, C++ as a compiled language performs the fastest, while Python as an interpreted language is the slowest. MATLAB, Julia, and R achieve a balance between performance and interpretability. As an early statistical analytics software, Stata was the most conventional programming language option. Economists embraced Stata as one of the most popular statistical analytics programs due to its breadth, accuracy, flexibility, and repeatability.


Journals

The following journals specialise in computational economics: ''ACM Transactions on Economics and Computation'', ''Computational Economics'', ''Journal of Applied Econometrics'', ''
Journal of Economic Dynamics and Control The ''Journal of Economic Dynamics and Control ''(JEDC) is a peer-reviewed scholarly journal devoted to computational economics, dynamic economic models, and macroeconomics. It is edited at the University of Amsterdam and published by Elsevier ...
''
Journal of Economic Dynamics and Control
', including Aims & scope link.  For a much-cited overview and issue, see:   • Leigh Tesfatsion, 2001. "Introduction to the Special Issue on Agent-based Computational Economics," ''Journal of Economic Dynamics & Control'', pp.

  • pecial issue 2001. ''Journal of Economic Dynamics and Control'', Agent-based Computational Economics (ACE). 25(3-4), pp. 281-654. Abstract/outlin
links
and the ''Journal of Economic Interaction and Coordination''.


References


External links


Society for Computational Economics

Journal of Economic Dynamics and Control
- publishes articles on computational economics

- maintained by Leigh Tesfatsion
The Use of Agent-Based Models in Regional Science
- a study on agent-based models to simulate urban agglomeration

- a series of lectures
Computational Finance and Economic Agents

Journal of Economic Interaction and Coordination
- official journal of the Association of Economic Science with Heterogeneous Interacting Agents
Chair of Economic Policy, University of Bamberg (Germany)Repository of public-domain computational solutions
{{Economics Mathematical economics Computational fields of study Mathematical and quantitative methods (economics)