econometrics n : the application of mathematics and statistics to the study of economic and financial data
branch of economics
- Chinese: 计量经济学 (jìliàng jīngjì xué)
- Czech: ekonometrie
- Danish: økonometri
- Dutch: econometrie
- Finnish: ekonometria
- French: économétrie
- German: Ökonometrie
- Greek, Modern: οικονομετρία (el)
- Italian: statistica economica
- Norwegian: econometrie
- Polish: ekonometria
- Portuguese: econometria
- Spanish: econometría
- Swedish: ekonometri
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Econometrics is concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of economic principles. Econometrics combines economic theory with statistics to analyze and test economic relationships. Theoretical econometrics considers questions about the statistical properties of estimators and tests, while applied econometrics is concerned with the application of econometric methods to assess economic theories. Although the first known use of the term "econometrics" was by Pawel Ciompa in 1910, Ragnar Frisch is given credit for coining the term in the sense that it is used today.
Although many econometric methods represent applications of standard statistical models, there are some special features of economic data that distinguish econometrics from other branches of statistics. Economic data are generally observational, rather than being derived from controlled experiments. Because the individual units in an economy interact with each other, the observed data tend to reflect complex economic equilibrium conditions rather than simple behavioral relationships based on preferences or technology. Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous equation models. Early work in econometrics focused on time-series data, but now econometrics also fully covers cross-sectional and panel data.
PurposeThe two main purposes of econometrics are to give empirical content to economic theory and to subject economic theory to potentially falsifying tests.
Data sets to which econometric analyses are applied can be classified as time-series data, cross-sectional data, panel data, and multidimensional panel data. Time-series data sets contain observations over time; for example, inflation over the course of several years. Cross-sectional data sets contain observations at a single point in time; for example, many individuals' incomes in a given year. Panel data sets contain both time-series and cross-sectional observations. Multi-dimensional panel data sets contain observations across time, cross-sectionally, and across some third dimension. For example, the Survey of Professional Forecasters contains forecasts for many forecasters (cross-sectional observations), at many points in time (time series observations), and at multiple forecast horizons (a third dimension).
Econometric analysis may also be classified on the basis of the number of relationships modelled. Single equation methods model a single variable (the dependent variable) as a function of one or more explanatory (or independent) variables. In many econometric contexts, such single equation methods may not recover the effect desired, or may produce estimates with poor statistical properties. Simultaneous equation methods have been developed as one means of addressing these problems. Many of these methods use variants of instrumental variable to make estimates.
Other important methods include Method of Moments, Generalized Method of Moments (GMM), Bayesian methods, Two Stage Least Squares (2SLS), and Three Stage Least Squares (3SLS).
ExampleA simple example of a relationship in econometrics from the field of labor economics is:
- \ln(\text) = \beta_0 + \beta_1 (\text) + \varepsilon.
Economic theory says that the natural logarithm of a person's wage is a linear function of the number of years of education that person has acquired. The parameter \beta_1 measures the increase in the natural log of the wage attributable to one more year of education. It should be noted that by using the natural log we have moved away from a simple linear regression model and are now using a non linear model, in this case, a semi-log y model. The term \epsilon is a random variable representing all other factors that may have direct influence on wage. The econometric goal is to estimate the parameters, \beta_0 \mbox \beta_1 under specific assumptions about the random variable \epsilon. For example, if \epsilon and Years of Education are uncorrelated, then the equation can be estimated with ordinary least squares.
If the researcher could randomly assign people to different levels of education, the data set thus generated would allow the econometrician to estimate the effect of changes in years of education on wages. In reality, those experiments cannot be conducted. Instead, the econometrician observes the years of education of and the wages paid to people who differ along many dimensions. Given this kind of data, the estimated coefficient on Years of Education in the equation above reflects both the effect of education on wages and the effect of other variables on wages, if those other variables were correlated with education. For example, people with more innate ability may have higher wages and higher levels of education. Unless the econometrician controls for innate ability in the above equation, the effect of innate ability on wages may be falsely attributed to the effect of education on wages.
The most obvious way to control for innate ability is to include a measure of ability in the equation above. Exclusion of innate ability, together with the assumption that \epsilon is uncorrelated with education produces a misspecified model. A second technique for dealing with omitted variables is instrumental variables estimation.
Notable econometriciansThe following are the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel recipients in the field of econometrics:
- Jan Tinbergen and Ragnar Frisch were awarded the first Nobel Prize for Economic Sciences in 1969 for having developed and applied dynamic models for the analysis of economic processes.
- Lawrence Klein, Professor of Economics at the University of Pennsylvania, was awarded in 1980 for his computer modeling work in the field.
- Trygve Haavelmo was awarded in 1989. His main contribution to econometrics was his 1944 article (published in Econometrica) "The Probability Approach to Econometrics."
- Daniel McFadden and James Heckman shared the award in 2000 for their work in microeconometrics. McFadden founded the econometrics lab at the University of California, Berkeley.
- Robert Engle and Clive Granger at the University of California, San Diego, were awarded in 2003 for work on analyzing economic time series. Engle pioneered the method of autoregressive conditional heteroskedasticity (ARCH) and Granger the method of cointegration.
The Econometric Author Links of the Econometrics Journal provides personal links to recent articles and working papers of econometric authors via the RePEc system in EconPapers.
JournalsThe main journals which publish work in econometrics are Econometrica, the Journal of Econometrics, the Review of Economics and Statistics, the Econometric Theory, the Journal of Applied Econometrics, the Econometric Reviews, and the Journal of Business and Economic Statistics .
- Handbook of Econometrics Elsevier, links to:
- v. 1, pp. 3-771 (1983)
- v. 2, pp. 775-1461 (1984)
- v. 3, pp. 1465-2107 (1986)
- v. 4, pp. 2111-3155 (1994)
- v. 5, pp. 3159-3843 (2001)
- v. 6, Part 1, pp. 3845-4776 (2007)
- v. 6, Part 2, pp. 4777-5752 (2007)
- v. 2, pp. 775-1461 (1984)
- Harry H. Kelejian and Wallace E. Oates (1989, 3rd ed.) Introduction to Econometrics.
- Peter Kennedy (2003). A Guide to Econometrics, 5th ed. Preview.
- Robert S. Pindyck and Daniel L. Rubinfeld (1998, 4th ed.).
- A.H. Studenmund (2000, 4th ed.) Using Econometrics: A Practical Guide.
- Correlation does not imply causation
- Choice Modelling
- Modeling and analysis of financial markets
- Important publications in econometrics
- MacKinnon, James and Davidson, Russell. Econometric Theory and Methods. New York: Oxford University Press, 2004.
- Wooldridge, Jeffrey. Introductory Econometrics: A Modern Approach. Mason: Thomson South-Western, 2003. ISBN 0-324-11364-1
- Gretl, the Gnu Regression, Econometrics and Time Series Library, open source and free software for econometrics..
- Hayashi, Fumio. Econometrics. Princeton University Press, 2000.
- Econometric Links
- Single equation methods (econometrics)
- Granger causality
- Augmented Dickey-Fuller test
- Unit root
- Applied Econometric Association
- Pearl, J. Causality: Models, Reasoning and Inference, Cambridge University Press, 2000.
- "The Art and Science of Cause and Effect": a slide show and tutorial lecture by Judea Pearl
econometrics in Arabic: اقتصاد قياسي
econometrics in Belarusian (Tarashkevitsa): Эканамэтрыка
econometrics in Catalan: Econometria
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econometrics in Danish: Økonometri (økonomi)
econometrics in German: Ökonometrie
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econometrics in Modern Greek (1453-): Οικονομετρία
econometrics in Spanish: Econometría
econometrics in Esperanto: Ekonometrio
econometrics in French: Économétrie
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econometrics in Japanese: 計量経済学
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econometrics in Simple English: Econometrics
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econometrics in Ukrainian: Економетрика
econometrics in Chinese: 计量经济学