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Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. [1] 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."
In econometrics, the seemingly unrelated regressions (SUR) [1]: 306 [2]: 279 [3]: 332 or seemingly unrelated regression equations (SURE) [4] [5]: 2 model, proposed by Arnold Zellner in (1962), is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially ...
Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments. [10] In this, the design of observational studies in econometrics is similar to the design of studies in other observational disciplines, such as astronomy, epidemiology, sociology and political science.
It is closely related to non-identifiability in statistics and econometrics, which occurs when a statistical model has more than one set of parameters that generate the same distribution of observations, meaning that multiple parameterizations are observationally equivalent.
The endogeneity problem is particularly relevant in the context of time series analysis of causal processes. It is common for some factors within a causal system to be dependent for their value in period t on the values of other factors in the causal system in period t − 1. Suppose that the level of pest infestation is independent of all ...
The journal was founded against a backdrop of strong growth in econometrics research in 1985. [4] At the time of its foundation, a main goal was to support theoretical developments in econometrics. Whereas many early articles focused exclusively on theory, disregarding practical applications, it became standard practice to include empirical ...
A variety of methods are used in econometrics to estimate models consisting of a single equation. The oldest and still the most commonly used is the ordinary least squares method used to estimate linear regressions. A variety of methods are available to estimate non-linear models.
However the converse is false: There exist point-estimation problems for which the minimum-variance mean-unbiased estimator is inefficient. [6] Historically, finite-sample efficiency was an early optimality criterion. However this criterion has some limitations: Finite-sample efficient estimators are extremely rare.