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Econometrics literally means 'economic measurement'. It is the branch of economics that applies statistical methods to the empirical study of economic theories and relationships. It is a combination of mathematical economics, statistics, economic statistics and economic theory.

The two main purposes of econometrics are to give empirical content to economic theory and also to empirically verify economic theory. For example, econometrics could empirically verify if indeed a given demand curve slopes downward as economic theory would suggest. Empirical content is also given in that a numerical value would be given to this slope, while economic theory alone is usually mute on actual specific values.

An econometrician often changes qualitative statements into a quantitative mathematical form that lends itself to measurement. These statements can then be empirically proven, disproven, measured, and compared. Econometrics differs from statistics done in other fields since controlled experiments are often impractical, so econometics has to frequently deal with data as is.

Arguably the most important tool of econometrics is regression analysis (for an overview of a linear implementation of this framework, see linear regression).

Econometric analysis can often be divided into time-series analysis and cross-sectional analysis. Time-series analysis examines variables over time, such as the effect of interest rates on national expenditure. Cross-sectional analysis studies relationship between different variables at a point in time. For instance, the relationship between income, locality, and personal expenditure. 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 pooled cross section data.

A simple example of a relationship in econometrics is:

Personal Expenditure = Propensity to Spend * Income + random error

This statement asserts that the amount a person spends is dependent on their income and their willingness to spend money. If we can observe personal expenditure and income, techniques such as regression analysis can then be applied to find the value of the coefficients, here just the propensity to spend. The estimated coefficient can then be compared across samples (such as different countries or income brackets) and conclusions made.

The above example can also be used to illustrate the many difficulties facing the applied econometrician. For instance, do we really know that the above relationship is correct? Perhaps the true relationship between personal expenditure and income is non-linear (that is, curved). Even if we know the correct theory, it is not certain we can meaure personal expenditure and income correctly. For instance, the value of work by e.g. homemakers is not recorded although it contributes to income. There are also a variety of statistical pitfalls that potentially lead to incorrect conclusions. Econometrics has dealt extensively with such issues. Often it turns out to be difficult to fully implement the resulting methods in practice.

In order to classify business and industry, econometricians rely on two main systems: SIC codes and more recently NAICS codes.


Nobel Prize for Economic Sciences recipients in the field of econometrics: