Computational economics

Computational Economics is an interdisciplinary research discipline that involves computer science, economics, as well as management science. This spoke encompasses computational modeling of economic systems. Some of these areas are unique, while others setting areas of economics by allowing robust data analytics and solutions of problems that would be arduous to research without computers and associated numerical methods.

Computational methods throw been applied in various fields of economics research, including but not limiting to:   

Econometrics: Non-parametric approaches, Semi-parametric approaches, and Machine Learning.

Dynamic Systems Modeling: Optimization, Dynamic stochastic general equilibrium modeling, and Agent-based modeling.

History

Computational economics developed concurrently with a mathematization of a field. During the early 20 century, pioneers such(a) as Jan Tinbergen and Ragnar Frisch advanced the computerization of economics and the growth of econometrics. As a sum of advancements in Econometrics, regression models, hypothesis testing, and other computational statistical methods became widely adopted in economic research. On the theoretical front, complex macroeconomic models, including the Real house Cycle RBC framework and Dynamic Stochastic General Equilibrium DSGE models make-up propelled the developing and application of numerical calculation methods that rely heavily on computation. In the 21^{st century, the coding of computational algorithms created new means for computational methods to interact with economic research. sophisticated approaches such(a) 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.}