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The term was crafted by Ragnar Frisch of Norway.
It evaluates the scope of Econometric theory that is about the development of methods and tools for repeated measurement on a set of variables like weekly earnings, age,
educational attainment and other descriptive characteristics that helps to create data, sample, and datasets that can be cross-sectional, clustered, panel, time series or spatial.
Time series are indexed by time and its examples are macroeconomic aggregates, interest rates, and prices.
Clustered are mutually independent but dependent within the cluster.
Spatial dependence is a model of interdependence.
Panel elements combine the cross-sections and time series.
It was argued that such a theory must necessarily be based on probability models. It should be explicitly designed in the manner to be able to incorporate randomness.
Economists used the structural approach to get likelihood-based analysis and a quasi structural approach that is based on approximation instead of truth, while, the calibration approach interprets the structural models as an approximation and inherently false.
The Applied econometrics term is used for the development of quantitative models and the application of various methods to these models by using the economic data.
MATLAB, GAUSS, and OxMetrics are some of the high-level matrix programming languages with built-in functions.
Economists make use of programming software with a set of pre-programmed statistical tools to update new methods to predict the overview of financial markets, the next economic crisis, or the alternative capital investment factors.
Econometricians can use R that is an open-source, user-contributed program or use a programming language like Fortran or C to get customized alternatives.
Some such software programs are available online that can help to provide a proper analysis of the data but its disadvantage is that one has to do much programming to detect and eliminate errors.
One of the most common tools used by econometricians is regression.
The simplest regression is a regression with a single explanatory variable.
For example –
In the case of income and education, it could be I = β0 + β1 E + ε,
I am called the dependent (endogenous) variable,
E is known as the explanatory (exogenous),
β0 and β1 are the regression co-coefficient,
And ε is the noise term.
This regression equation will put a straight line through the data.
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