Econometrics may use standard statistical models to study economic questions, but most often they are with observational data, rather than in controlled experiments.
Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous-equation models.
Such methods may allow researchers to estimate models and investigate their empirical consequences, without directly manipulating the system.
Observational data may be subject to omitted-variable bias and a list of other problems that must be addressed using causal analysis of simultaneous-equation models.
[13] In recent decades, econometricians have increasingly turned to use of experiments to evaluate the often-contradictory conclusions of observational studies.
Here, controlled and randomized experiments provide statistical inferences that may yield better empirical performance than do purely observational studies.
Cross-sectional data sets contain observations at a single point in time; for example, many individuals' incomes in a given year.
[17] Such concerns include mathematical well-posedness: the existence, uniqueness, and stability of any solutions to econometric equations.
The benefit of this approach is that, provided that counter-factual analyses take an agent's re-optimization into account, any policy recommendations will not be subject to the Lucas critique.
Structural econometric analyses begin with an economic model that captures the salient features of the agents under investigation.
[21] Another example of structural econometrics is in the estimation of first-price sealed-bid auctions with independent private values.