Applied Econometrics: concepts

Applied econometrics focuses on the practical implementation of statistical and econometric techniques to analyze economic data, test hypotheses, and evaluate models to inform decision-making.

 I write this session focus on three goals:

1. Clarify key concepts with a focus on practical implementation.
2. Highlighted the tools and methods typically employed.
3. Linked the concepts to the research or policy contexts (Inform decision-making).
   

 

Sample change in the context of econometrics models

The sample changes can affect the results that we had been observed.  The estimates parameters or coefficients will be affected and change, for example if we removed observations that are outlier. The variance and standard errors will change depending on the variability of the new observations; and this change will affect the hypothesis tests, with impact in the p-values and confidence intervals. Otherwise if the new observations aligns better with the trend the model fit of the model will increase, if we consider the model assumptions the predictive accuracy could being improve or degrade. This changes go to be under the condition of the sensitivity of the model to this individual data points.

Full Rank

A matrix is full rank if the number of linearly independent rows (or columns) is maximized, and this property influences whether the matrix is invertible or has certain other properties like the existence of a unique solution to a linear system.

In econometrics, the assumption of full rank implied that there is not linear relationship between the independent variables.