Editing Computational economics (section)

== Applications ==

=== Agent based modelling ===
{{Main|Agent-based model}}

Computational economics uses computer-based [[economic model]]ing to solve analytically and statistically formulated economic problems. A research program, to that end, is [[agent-based computational economics]] (ACE), the computational study of economic processes, including whole [[Economy|economies]], as [[dynamic system]]s of interacting [[Agent (economics)|agents]].<ref name=":23">• Scott E. Page, 2008. "agent-based models," ''The New Palgrave Dictionary of Economics'', 2nd Edition. [http://www.dictionaryofeconomics.com/article?id=pde2008_A000218&edition=current&q=agent-based%20computational%20modeling Abstract]. &nbsp;&nbsp; • Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, ''Handbook of Computational Economics'', v. 2, [pp. 831-880]. {{doi|10.1016/S1574-0021(05)02016-2}}. &nbsp;&nbsp; • Kenneth L. Judd, 2006. "Computationally Intensive Analyses in Economics," ''Handbook of Computational Economics'', v. 2, ch. 17, pp. [https://books.google.com/books?id=6ITfRkNmKQcC&pg=PA881 881-] 893. Pre-pub [https://www2.econ.iastate.edu/tesfatsi/Judd.finalrev.pdf PDF]. &nbsp;&nbsp; • L. Tesfatsion and K. Judd, ed., 2006. ''Handbook of Computational Economics'', v. 2, ''Agent-Based Computational Economics'', Elsevier. [http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description Description] {{Webarchive|url=https://web.archive.org/web/20120306100156/http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description|date=2012-03-06}} & and chapter-preview [http://www.sciencedirect.com/science?_ob=PublicationURL&_hubEid=1-s2.0-S1574002105X02003&_cid=273377&_pubType=HS&_auth=y&_acct=C000228598&_version=1&_urlVersion=0&_userid=10&md5=e4757b4f65755ed6340a11fee9615200 links]. &nbsp;&nbsp; • [[Thomas J. Sargent]], 1994. ''Bounded Rationality in Macroeconomics'', Oxford. [http://www.oup.com/us/catalog/general/subject/Economics/MacroeconomicTheory/?view=usa&ci=9780198288695 Description] and chapter-preview 1st-page [https://www.questia.com/library/book/bounded-rationality-in-macroeconomics-thomas-j-sargent-by-thomas-j-sargent.jsp links.]</ref> As such, it is an economic adaptation of the [[complex adaptive system]]s [[paradigm]].<ref name=":33">• [[W. Brian Arthur]], 1994. "Inductive Reasoning and Bounded Rationality," ''American Economic Review'', 84(2), pp. [http://www-personal.umich.edu/~samoore/bit885f2011/arthur-inductive.pdf 406-411] {{Webarchive|url=https://web.archive.org/web/20130521145936/http://www-personal.umich.edu/~samoore/bit885f2011/arthur-inductive.pdf|date=2013-05-21}}. &nbsp;&nbsp; • [[Leigh Tesfatsion]], 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," ''Information Sciences'', 149(4), pp. [http://copper.math.buffalo.edu/urgewiki/uploads/Literature/Tesfatsion2002.pdf 262-268] {{webarchive|url=https://web.archive.org/web/20120426000037/http://copper.math.buffalo.edu/urgewiki/uploads/Literature/Tesfatsion2002.pdf|date=April 26, 2012}}. &nbsp;&nbsp; • _____, 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," ''Artificial Life'', 8(1), pp.55-82. [http://www.mitpressjournals.org/doi/abs/10.1162/106454602753694765 Abstract] and pre-pub [http://www.econ.brown.edu/fac/Peter_Howitt/SummerSchool/Agent.pdf PDF] {{webarchive|url=https://web.archive.org/web/20130514143904/http://www.econ.brown.edu/fac/Peter_Howitt/SummerSchool/Agent.pdf|date=2013-05-14}}.</ref> Here the "agent" refers to "computational objects modeled as interacting according to rules," not real people.<ref name="Page200824"/> Agents can represent social, biological, and/or physical entities. The theoretical assumption of [[mathematical optimization]] by agents in [[Equilibrium (economics)|equilibrium]] is replaced by the less restrictive postulate of agents with [[bounded rationality]] ''adapting'' to market forces,<ref name=":43">• W. Brian Arthur, 1994. "Inductive Reasoning and Bounded Rationality," ''American Economic Review'', 84(2), pp. [http://www-personal.umich.edu/~samoore/bit885f2011/arthur-inductive.pdf 406-411] {{Webarchive|url=https://web.archive.org/web/20130521145936/http://www-personal.umich.edu/~samoore/bit885f2011/arthur-inductive.pdf|date=2013-05-21}}. &nbsp;&nbsp; • [[John H. Holland]] and John H. Miller (1991). "Artificial Adaptive Agents in Economic Theory," ''American Economic Review'', 81(2),  pp. [http://www.santafe.edu/media/workingpapers/91-05-025.pdf 365-370] {{Webarchive|url=https://web.archive.org/web/20110105015853/http://www.santafe.edu/media/workingpapers/91-05-025.pdf|date=2011-01-05}}. &nbsp;&nbsp; • [[Thomas C. Schelling]], 1978 [2006].  ''Micromotives and Macrobehavior'', Norton. [http://books.wwnorton.com/books/978-0-393-32946-9/ Description] {{Webarchive|url=https://web.archive.org/web/20171102093240/http://books.wwnorton.com/books/978-0-393-32946-9/|date=2017-11-02}},  [https://books.google.com/books?id=DenWKRgqzWMC&pg=PA1= preview]. &nbsp;&nbsp; •  [[Thomas J. Sargent]], 1994. ''Bounded Rationality in Macroeconomics'', Oxford. [http://www.oup.com/us/catalog/general/subject/Economics/MacroeconomicTheory/?view=usa&ci=9780198288695 Description] and chapter-preview 1st-page [https://www.questia.com/library/book/bounded-rationality-in-macroeconomics-thomas-j-sargent-by-thomas-j-sargent.jsp links.]</ref> including [[Game theory|game-theoretical]] contexts.<ref name="COMP&GT23">• [[Joseph Y. Halpern]], 2008. "computer science and game theory," ''The New Palgrave Dictionary of Economics'', 2nd Edition.  [http://www.dictionaryofeconomics.com/article?id=pde2008_C000566&edition=current&q=&topicid=&result_number=1 Abstract]. &nbsp;&nbsp; • Yoav Shoham, 2008. "Computer Science and Game Theory," ''Communications of the ACM'',  51(8), pp. [http://www.robotics.stanford.edu/~shoham/www%20papers/CSGT-CACM-Shoham.pdf 75-79] {{Webarchive|url=https://web.archive.org/web/20120426005917/http://www.robotics.stanford.edu/~shoham/www%20papers/CSGT-CACM-Shoham.pdf|date=2012-04-26}}. &nbsp;&nbsp; • [[Alvin E. Roth]], 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," ''Econometrica'', 70(4), pp. [http://kuznets.fas.harvard.edu/~aroth/papers/engineer.pdf 1341–1378] {{webarchive|url=https://web.archive.org/web/20040414102216/http://kuznets.fas.harvard.edu/~aroth/papers/engineer.pdf|date=2004-04-14}}.</ref> 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.<ref name=":53">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 [pp. 831-880]. {{doi|10.1016/S1574-0021(05)02016-2}}.</ref>

=== Machine learning in computational economics ===
[[Machine learning|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]] have been developed and utilized in [[Data mining|data-mining]] and statistical analysis. These models provide superior classification, predictive capabilities, flexibility compared to traditional statistical models, such as that of the [[STAR model|STAR]] method. Other methods, such as causal machine learning and [[Causal model|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 (statistics)|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, [[Causal inference|statistical causal inference]], a core focus of modern empirical research. For example, economics researchers might hope to identify [[Confounding|confounders]], [[confidence interval]]s, and other parameters that are not well-specified in Machine Learning algorithms.<ref name=":93">{{Citation |title=The Impact of Machine Learning on Economics |date=2019 |url=http://dx.doi.org/10.7208/chicago/9780226613475.003.0021 |work=The Economics of Artificial Intelligence |pages=507–552 |publisher=University of Chicago Press |doi=10.7208/chicago/9780226613475.003.0021 |isbn=9780226613338 |s2cid=67460253 |access-date=2022-05-05|last1=Athey |first1=Susan |url-access=subscription }}</ref>

Machine learning may effectively enable the development of more complicated [[Heterogeneity in economics|heterogeneous]] 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).<ref name=":103">{{Cite book |last=Jesus |first=Browning, Martin Carro |url=http://worldcat.org/oclc/1225293761 |title=Heterogeneity and microeconometrics modelling |date=2006 |publisher=CAM, Centre for Applied Microeconometrics |oclc=1225293761}}</ref> 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.<ref name=":113">{{Cite journal |last1=Charpentier |first1=Arthur |last2=Élie |first2=Romuald |last3=Remlinger |first3=Carl |date=2021-04-23 |title=Reinforcement Learning in Economics and Finance |url=https://doi.org/10.1007/s10614-021-10119-4 |journal=Computational Economics |language=en |doi=10.1007/s10614-021-10119-4 |arxiv=2003.10014 |s2cid=214612371 |issn=1572-9974}}</ref>

The adoption and implementation of [[neural network]]s, [[deep learning]] in the field of computational economics may reduce the redundant work of [[Data cleansing|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.<ref name=":73">{{Cite journal |last1=Farrell |first1=Max H. |last2=Liang |first2=Tengyuan |last3=Misra |first3=Sanjog |date=2021 |title=Deep Neural Networks for Estimation and Inference |journal=Econometrica |volume=89 |issue=1 |pages=181–213 |doi=10.3982/ecta16901 |s2cid=203696381 |issn=0012-9682|doi-access=free |arxiv=1809.09953 }}</ref> 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.<ref name=":83">{{Cite journal |date=2021-07-27 |title=Deep learning for individual heterogeneity: an automatic inference framework |doi=10.47004/wp.cem.2021.2921 |s2cid=236428783 |doi-access=free }}</ref>  

=== Dynamic stochastic general equilibrium (DSGE) model ===
{{Main|Dynamic stochastic general equilibrium|l1=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 choice|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. Typical programming languages used in computational economics research include [[C++]], [[MATLAB]], [[Julia (programming language)|Julia]], [[Python (programming language)|Python]], [[R (programming language)|R]] and [[Stata]].

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.