Econometric model

 

Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining a particular economic phenomena under study. An econometric model can be derived from a deterministic economic model by allowing for uncertainty or from an economic model which itself is stochastic. However, it is also possible to use econometric models that are not tied to any specific economic theory.^[1]

Contents [hide] 1 Formal definition 2 Basic models 3 Use in policy-making 4 References 5 External links 6 Notes

[edit] Formal definition

In econometrics, as in statistics in general, it is presupposed that the quantities being analyzed can be treated as random variables. An econometric model then is a set of joint probability distributions to which the true joint probability distribution of the variables under study is supposed to belong. In case the elements of this set can be indexed by a finite number of real-valued parameters, the model is called a parametric model, otherwise it is a nonparametric or semiparametric model. A large part of econometrics is the study of methods for the selecting models, estimating them, and carrying out inference on them.
The most common econometric models, however, are not statistical but structural, for they convey causal and counterfactual information^[2], and are used for policy evaluation rather than statistical predictions

[edit] Basic models

Some of the common econometric models are:

• Linear regression
• Generalized linear models
• Tobit
• ARIMA
• Vector Autoregression
• Cointegration

[edit] Use in policy-making

Comprehensive models of macroeconomic relationships are used by central banks and governments to evaluate and guide economic policy. Two famous econometric models of this nature are the Federal Reserve Bank econometric model and the DRI-WEFA (now Global Insight) model.


http://en.wikipedia.org/wiki/Econometric_model