Dear all,

I have a dataset with a dependent variable y and three independent variables x1 x2 x3. Since my dependent variable y is U-shaped, an OLS regression is not sufficient to predict the values of y. Therefore, I want to predict the values with a quantile regression. Is there a command that predicts the y values accurately across the whole distribution of y?

In the end I want to:

1. get a variable ypred that has a similar density (U-shaped) as variable y (contrary to an OLS regression which in this case is normally distributed). So that kdensity ypred looks similar to kdensity y.

2. generate a new variable which looks like this in case of an OLS regression: gen ystressed = _b[_cons] + _b[x1]*x1 + _b[x2]*x2 + _b[x3]*z with z being a fixed number.

I know how to get those variables in case of an OLS regression but I am not sure how to get this variables in case of a quantile regression.

Kind regards,

Steffen

I have a dataset with a dependent variable y and three independent variables x1 x2 x3. Since my dependent variable y is U-shaped, an OLS regression is not sufficient to predict the values of y. Therefore, I want to predict the values with a quantile regression. Is there a command that predicts the y values accurately across the whole distribution of y?

In the end I want to:

1. get a variable ypred that has a similar density (U-shaped) as variable y (contrary to an OLS regression which in this case is normally distributed). So that kdensity ypred looks similar to kdensity y.

2. generate a new variable which looks like this in case of an OLS regression: gen ystressed = _b[_cons] + _b[x1]*x1 + _b[x2]*x2 + _b[x3]*z with z being a fixed number.

I know how to get those variables in case of an OLS regression but I am not sure how to get this variables in case of a quantile regression.

Kind regards,

Steffen

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