Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". The first known use of the term "econometrics" was by Polish economist Paweł Ciompa in 1910. Jan Tinbergen is considered by many to be one of the founding fathers of econometrics. Ragnar Frisch is credited with coining the term in the sense in which it is used today.

A person who uses statistics and mathematics to study, model and predict economic principles and outcomes. Econometricians use statistical measures and mathematical formulas to produce objective results in the study of economics.

An econometrician is a type of economist who integrates statistics and mathematics into economic analysis. Econometricians use highly specialized math and statistics to generate quantifiable results. Individuals employed as econometricians typically have advanced degrees in statistics and/or economics, although some universities do offer specific degrees in econometrics.

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An econometric model is a statistical model used in economics.

An econometric model specifies the relationship that is believed to hold between various economic quantities pertaining to a particular economic phenomenon.

By allowing for uncertainty, or by using stochastic elements.

Errors and residuals in statistics; random variables; stochastic processes (such as Brownian motion); and time-series data containing trends, seasonality, etc.

Yes, there are many different types of models that can be used as the basis for creating an econometric model.

Examples include linear regression models, nonlinear regression models (including generalized additive models), time series analysis (including vector autoregression), spatial analysis (including Bayesian geostatistics), multivariate analysis (including factor analysis and principal component analysis) and panel data methods such as fixed effects estimation or instrumental variables estimation.

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