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# econometrics

Contributor(s): Matthew Haughn

Econometrics is the analysis and testing of economic theories to verify hypotheses and improve prediction of financial trends. Econometrics takes mathematical and statistical models proposed in economic theory and tests them. First, models are tested against statistical trials, followed by testing against real-world examples to support or disprove hypotheses.

Econometrics uses an important statistical method called regression analysis, which assesses the connection among variables. Economists use the regression method since they cannot usually carry out controlled experiments, choosing to instead gather information from natural experiments. The method typically includes the following steps:

1. Subject data is examined to attempt to understand the data set. In that examination, theories and hypotheses are formed as to why the data manifests the way it does to explain the variables being analyzed.
2. Models and statistical tools for testing the hypothesis are selected. The relationships between this step, the explanatory variable and the dependent variable are summarized.
3. Data is input into economic software and the selected statistical models are applied to estimate results from the supplied data.
4. Results are collected and portions are analyzed to obtain economic predictions. If the results are as expected, the hypothesis is generally considered validated. Otherwise, new hypotheses need to be found.

Real world applications of econometrics include the study of the income effect, defined as the change in demand for a product or service resulting from a change in income.

Lawrence Klein, Simon Kuznets and Ragnar Frisch first conceived of econometrics as a field of study. All three economists received the Nobel Prize in economics for their founding work.

This was last updated in February 2018

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