Quantile Regression Intro - with reference to Eviews and R (Applied Econometrics)

 ■ Quantile Regression

Basically, Quantile regression is an extension of linear regression. It's used when the conditions of linear regressions are not met like linearity, homoscedasticity, normality.

The Quantile regression has no strong distributional assumptions.

Let's state Hypotheses.

Null (Ho): There's no significant impact on dependent variable due to independent variables.

Say that, the natural log of one of the independent variables is statisically significant, since p values is less than 0.05. If there's an increase in 1% median value of independent variables will inceease by 1.13% in the median value.

And in addition, remaining independent variable is not significant since p value is greater than 0.05.

■ Quantile regression's Goodness of Fit

The Pseudo R ^ 2 is 31 % (assume). The adjusted R ^ 2 say, is 28 %. So 28 % variation in conditional median in the dependent variable is due to the one of the independent variables.

The Quasi LR statistic value is say (29.3) and the p value is less than 0.05 that indicates that the model is stable.

■ The process of Quantile Regression (E views - with reference with R will be discussed in the next part of this blog)

1st run the linear regression and test for the serial correlations, normality and homoscedasticity. Make sure these three are insignificant. Thus, with these assumption we go for the Quantile Regression.

Estimate Eqn ➡ Method ➡ Quant Reg ➡Click ok

Choose the Quantile to estimate ➡ Select the value from 0 to 1 then Click OK.

Note: In linear regression its mean and in quantile regression its median.

We shall run the results and see results and interpreted.

For the results, Go to View ➡ Quantile Regression ➡ Process Coefficients ➡ Table ➡ Set Quantile 10 ➡ Click OK.

The result will be intrepreted in next blog.

To be contd...

Thank You

Aditya Pokhrel

MBA, MA Economics, MPA