Learn more about Econometrics
The two main purposes of econometrics are to give empirical content to economic theory and to subject economic theory to potentially falsifying tests. For example, economic theory may predict that a given demand curve should slope down. Econometric estimates can either verify or falsify that prediction, and shed light on the magnitude of the effect.
The most important statistical method in econometrics is regression analysis. For an overview of a linear implementation of this framework, see linear regression. Regression methods are important in econometrics because economists typically cannot use controlled experiments. Observational data are often subject to lurking variable and other problems which must be addressed statistically using regression models. Econometricians often seek illuminating natural experiments in the absence of evidence from controlled experiments.
Econometric analysis is divided into time-series analysis and cross-sectional analysis. Time-series analysis examines variables over time, such as the effects of population growth on a nation's GDP. Cross-sectional analysis examines the relationship between different variables at a point in time; for instance, the relationship between individuals' income and food expenditures. When time-series analysis and cross-sectional analysis are conducted simultaneously on the same sample, it is called panel analysis. If the sample is different each time, it is called repeated cross section data. Multi-dimensional panel data analysis is conducted on data sets that have more than two dimensions. For example, some forecast data sets provide forecasts for multiple target periods, conducted by multiple forecasters, and made at multiple horizons. The three dimensions provide more information than can be gleaned from two dimensional panel data sets.
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 variables. In many econometric contexts such single equation methods may not be able to recover estimates of causal relationships because either the dependent variable causes changes in one of the explanatory variables or because variables not included in the model cause both the dependent and at least one of the independent variables. Simultaneous equation methods have been developed as one means of addressing these problems. Many of these methods use variants of instrumental variables models to make estimates.
Much larger econometric models are used in an attempt to explain or predict the behavior of national economies.
A simple example of a relationship in econometrics is:
- wage = constant + (rate of return to education) * education + random error
In this equation, a person's wage is a linear function of the number of years of education he has. The econometric goal is to estimate the expected change in wages a person would receive if she obtained one more year of education.
If the researcher could randomly assign people to differing levels of education, the correlation between education and wages would reveal the causal effect of education on wages. But it is not feasible to conduct such experiments. Instead the econometrician only observes how many years of education people obtain, and the wages they receive. The correlation between wages and education reflects both the effect of education on wages and unobserved variables which may affect both outcomes. For example, more intelligent people may tend to obtain more education and may also earn more at any level of education than less intelligent people. Econometric methods could be used to overcome these problems and estimate the underlying causal effect of education on wages.<ref>David Card. "The Causal Effect of Education on Earnings." In Orley Ashenfelter and David Card, editors, Handbook of Labor Economics Volume 3. Amsterdam: Elsevier, 1999.</ref>
Nobel Memorial Prize in Economics recipients in the field of econometrics:
- Jan Tinbergen and Ragnar Frisch were awarded in 1969 (the first Nobel Prize for Economic Sciences) 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 were awarded in 2003 for work on analysing 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
- List of statistical packages
- Comparison of statistical packages
- R programming language
 See also
- Correlation does not imply causation
- Modeling and analysis of financial markets
- Important publications in econometrics
- Wooldridge, Jeffrey. Introductory Econometrics: A Modern Approach. Mason: Thomson South-Western, 2003. ISBN 0-324-11364-1
- Hayashi, Fumio. Econometrics. Princeton University Press, 2000.
- Econometric Links
ca:Econometria da:Økonometri (økonomi) de:Ökonometrie el:Οικονομετρία es:Econometría eo:Ekonometrio fr:Économétrie id:Ekonometri it:Econometria he:אקונומטריקה hu:Ökonometria nl:Econometrie ja:計量経済学 no:Økonometri nn:Økonometri pl:Ekonometria pt:Econometria ru:Эконометрика simple:Econometrics su:Ékonométri fi:Ekonometria sv:Ekonometri tr:Ekonometri zh:计量经济学