The set of observations that reflect certain characteristics are known as Data. There are two types of data in regards to parametric vs nonparametric data, which are as follows:
Econometrics – Parametric vs nonparametric data
In econometrics quantitative research and analysis, the definition of parametric and nonparametric as a different meaning. Following are the definitions that best explains Parametric Data and non-parametric data in Econometrics and quantitative research & analysis.
The data that needs a vector to be defined with its magnitude is called a parametric data. For example to define GDP, it requires the currency to support the data. Suppose the GDP of XYZ country in year 2015 is 1.2 million dollar. So the dollar sign over here acts as a vector and therefore GDP is a parametric data. Data which is derived from parametric data, though having no vector, will also be known as parametric data. For example, GDP growth has no vector part, but it is derived from the last year GDP and the current year GDP, therefore GDP growth will also be a parametric data.
The data that does not required vector is called nonparametric data. For example, there are 5 students in a course. Here the data does not requires any vector to be defined and therefore known as nonparametric data. Another example of nonparametric data can be the level of satisfaction with a product in a scale of 1 to 5.
In reality, parametric data and procedures require more assumption as compared to the non-parametric data and procedures. If these assumption of parametric data and procedures are correct than the estimated results are more accurate and this will then be noted as most powerful statistical tool. However, on the other hand if these assumptions are not correct then the results will be highly incorrect.
Parametric data and procedures are mostly used due to their simple formulas and fast computing techniques.
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