Introduction
The application of different types of statistical techniques in the domain of economics for the purpose of data modeling and forecasting is called econometrics. Econometrics relies on quantitative data sets for testing hypotheses that aim to prove various concepts related to economics. After harnessing powerful data sets from real-world economic domains, various statistical metrics are applied to derive perspective from them. In this way, with the help of various classification and regression models, the scope of economics and finance is being expanded.
The linkages between data science and econometrics
When we use different types of machine learning and statistical methods for the further development of economic theories, we are navigating in the domain of econometrics. Econometrics also uses different types of frequency distribution tables to explain concepts and correlation techniques to develop economic models. These models serve as a repository of data for statisticians and mathematicians and other types of subject experts in economics. One of the most important techniques of machine learning that is used by economists worldwide is regression analysis. The time series analysis of data also helps in testing specific hypotheses and analyzing surveys related to consumer behavior, finances, market cycles, unemployment, and inflation rates. The application of various machine learning techniques in stock market analysis is particularly interesting. With the help of various multiple linear regression models, the dynamics of the stock market can be predicted and investment in shares that give high returns in the future can be channelized.
Overcoming the limitations of econometrics with data science
One of the major criticisms that econometrics has meted out in the past is related to the interpretation of raw and unstructured data. Although various statistical tools have been used to analyze and interpret raw data sets, economists are skeptical about the correlation of such data sets with established economic theories. As such, the deviation from basic economic theories is considered a major limitation of econometrics. Data analytics courses help in overcoming these limitations as they stick to the basic economic theory while simultaneously applying advanced algorithms and machine learning techniques for precise analysis. Data science broadens the scope of econometrics in three major domains. Firstly, data science helps in improving the problem-solving methodology of economics. Secondly, it helps in improving the tools used in economics. Thirdly, data science helps in broadening the range of applications of various economic models.
Problem-solving approach
For modeling human behavior, economists resorted to various theories that were qualitative in nature. As such, these theories were divided into mathematical modeling and statistical tools. What was needed was to quantify such theories so that the problem-solving approach of economic models becomes better.
This is where the role of data science comes into play. Data science helps in two main disciplines of economics that include microeconomics and macroeconomics. With the help of data science, micro models can be set up that help in understanding the basic interactions between building blocks of a society. On the other hand, macroeconomics helps in understanding the major details of an economic system as it links the functional blocks of an economy and makes it an interconnected system.
Widening of application domains
Data science has broadened the application domains of economics. This can better be understood when we look at the subsections of economics. Firstly, data science has helped to identify consumer behavior and track their marketing experience and engagement within an enterprise. This has helped in better targeting marketing campaigns and has also led to cost optimization with the help of data analytics models. Secondly, data science has helped in drafting appropriate insurance policies, soil monitoring, and weather prediction when it comes to agricultural economics. In addition to this, data science has also helped in supply chain networks and logistics facilities which have bridged the gap between the market and the farm. When it comes to industrial economics, data science has heralded a new dawn. Similarly, data science has enabled massive commodity production and product targeting as per the dynamics of the market. This is especially true for AI-engineered products which are in natural demand in the global market.
Concluding remarks
In one word, data science has improved the various types of methodologies and techniques and has made economics a quantitative discipline that is also data-driven. Ranging from policymaking to the analysis of the stock market, data science has made rapid advances in almost all domains of economics. Finally, data science has provided economics with an analytical toolbox that is changing the way economic theories operated in the past.
