Econometrics
[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.
"[2] An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships.
[7] A basic tool for econometrics is the multiple linear regression model.
[9][10] Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency.
A basic tool for econometrics is the multiple linear regression model.
This relationship is represented in a linear regression where the change in unemployment rate (
The model could then be tested for statistical significance as to whether an increase in GDP growth is associated with a decrease in the unemployment, as hypothesized.
[9][10] Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency.
Estimators that incorporate prior beliefs are advocated by those who favour Bayesian statistics over traditional, classical or "frequentist" approaches.
[13] Economics often analyses systems of equations and inequalities, such as supply and demand hypothesized to be in equilibrium.
Consequently, the field of econometrics has developed methods for identification and estimation of simultaneous equations models.
Such methods may allow researchers to estimate models and investigate their empirical consequences, without directly manipulating the system.
[14] The methods include regression discontinuity design, instrumental variables, and difference-in-differences.
A simple example of a relationship in econometrics from the field of labour economics is: This example assumes that the natural logarithm of a person's wage is a linear function of the number of years of education that person has acquired.
measures the increase in the natural log of the wage attributable to one more year of education.
Instead, the econometrician observes the years of education of and the wages paid to people who differ along many dimensions.
Another technique is to include in the equation additional set of measured covariates which are not instrumental variables, yet render
[16] The main journals that publish work in econometrics are: Like other forms of statistical analysis, badly specified econometric models may show a spurious relationship where two variables are correlated but causally unrelated.
In a study of the use of econometrics in major economics journals, McCloskey concluded that some economists report p-values (following the Fisherian tradition of tests of significance of point null-hypotheses) and neglect concerns of type II errors; some economists fail to report estimates of the size of effects (apart from statistical significance) and to discuss their economic importance.
She also argues that some economists also fail to use economic reasoning for model selection, especially for deciding which variables to include in a regression.
[25][26] In some cases, economic variables cannot be experimentally manipulated as treatments randomly assigned to subjects.
[27] In such cases, economists rely on observational studies, often using data sets with many strongly associated covariates, resulting in enormous numbers of models with similar explanatory ability but different covariates and regression estimates.
Regarding the plurality of models compatible with observational data-sets, Edward Leamer urged that "professionals ... properly withhold belief until an inference can be shown to be adequately insensitive to the choice of assumptions".
