Econometrics is derived from several disciplines, including mathematical economics, statistics, economic statistics, and economic theory. The goal of econometrics is twofold: to give economic theory empirical data and to empirically verify it. It is a study that produces measurements, where qualitative data is turned into quantitative mathematical forms. Once this is performed, these statements can then be empirically proven, disproven, measured, and compared.
Despite common assumption, econometrics is not statistics, differing in one important aspect: the study of statistics is performed in controlled experiments, whereas econometrics often has to deal with data as is. An important tool used in econometrics is regression analysis, which can further be divided into time-series analysis, and cross-sectional analysis. Time-series analysis examines and measures variables over a certain period of time. Cross-sectional analysis studies the correlation between different variables at a point in time. Time-series and cross-sectional analysis can be performed simultaneously on the same sample, which is then called panel analysis.
However, like many statistical methods, econometrics has garnered several criticisms, especially in the 1970s. Econometrics can produce inaccurate results if, for instance, a relationship between two variables is measured linearly when it should be curved. There are several statistical pitfalls that are difficult to account for within econometric models, which can lead to forecast failures (as in the Asian Crisis of 1997-98).
Another growing concern that has been raised about econometric models is the purposeful misrepresentation of data. Models only show the relationship between two variables and the conditions under which they occurred without explaining the reasons why these conditions or relationships happen, therefore results can be subject to several interpretations. Public reliance on statistics and numbers as legitimate can often be taken advantage of in order to present a viewpoint or push an agenda.
Nevertheless, econometric models are useful in predicting data rather than random guessing. They are an invaluable tool within the stock market and budget planning, but as with all presented data, consumers need to understand who is presenting that data and for what possible purpose. It must also be understood that predictive models forecast trends based only on existing data used to create them, usually without ever having to stand the test of time, so to speak.
The technique of econometrics is generally attributed to Norwegian economist Ragnar Frisch, who wrote several important studies on the subject in the mid-twentieth century as well as founded the Econometric Society. The study of econometrics rose in popularity in the 1930s along with the broad new field of economics: macroeconomics. These new studies have had a significant role in government economic planning and accounting. In 1969, Fisch and his partner Jan Tinbergen won the first Nobel Memorial Prize in Economic Sciences for their work.
Another significant econometrician is Lawrence Klein, who was awarded a Nobel Prize in 1980 for his contributions to the economic field. Klein helped to develop computer models to forecast economic trends in the field of econometrics that are still widely used today. He used his models to correctly predict the economic upturn after World War II as well as the mild recession after the Korean War, despite prevailing expectations.
Econometrics is an important area of study because it strives to ground mere theory into solid, empirical evidence that can then be tested. It can be used for practical application and planning of everything from local government budgets to international trade. Advanced technology has made forecasting models even more precise than ever before and is continually being improved to decrease margins of error. While it is not a perfect system and can be used for less than factual purposes, econometrics provides an important foundation for consumers, businesses, and governments.
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