 # Specifying quantiles for quantile regression

### Goals

This tutorial introduces the use of the optional input, tau, to specify quantile levels for the quantileFitprocedure.

## The tau input

The quantileFit procedure accepts the optional input, tau, as the third input:

quantileFit(dataset, formula, tau)

or

quantileFit(y, x, tau)

GAUSS accepts a single quantile level or a vector of quantile levels with values $0 \lt τ \lt 1$. By default, GAUSS estimates the regression for the 5%, 50%, and 95%. If you want estimates for these quantile levels you do not need to use the tau input. However, if you wish to change these quantile levels, you will need to specify custom tau levels.

## Basic example

Consider the example from our previous tutorial where we estimated:

$$ln(wage) = \alpha + \beta_1 * age + \beta_2 * age^2 + \beta_3 * tenure .$$

### Specifying a single quantile level

First, let's estimate the model for the 35% quantile level:

// Create string with full path to dataset
dataset = getGAUSSHome() $+ "examples/regsmpl.dta"; // Specify quantile level tau = 0.35; // Estimate the model with matrix inputs call quantileFit(dataset, "ln_wage ~ age + age:age + tenure", tau); This produces the following results : Total observations: 28101 Number of variables: 3 VAR. / tau (in %) 35% ------------------------------- CONSTANT 0.6846 age 0.0471 age:age -0.0008 tenure 0.0471 ### Comparing multiple quantile level Now, let's compare estimates of the model for the 35%, 50%, and 85% quantile levels: // Create string with full path to dataset dataset = getGAUSSHome()$+ "examples/regsmpl.dta";

// Specify quantile level
tau = 0.35|0.55|0.85;

// Estimate the model with matrix inputs
call quantileFit(dataset, "ln_wage ~ age + age:age + tenure", tau);

This produces the following output:

Total observations:                                   28101
Number of variables:                                      3

VAR. / tau (in %)       35%       55%       85%
---------------------------------------------------
CONSTANT            0.6846     0.4234     0.1441
age                 0.0471     0.0739     0.1081
age:age            -0.0008    -0.0011    -0.0014
tenure              0.0471     0.0457     0.0285

The output tables now contains three columns, one for each quantile level estimated.

### Conclusion

This tutorial showed you how to specify the quantile levels to be estimated when using quantileFit. You should now know how to:

• Specify a single quantile levels for use with quantileFit
• Specify multiple quantile levels for use with quantileFit

In the next tutorial we will learn how perform weighted analysis using the quantileFit procedure.

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