Quantile Regression

docs/_quarto.yml

@@ -45,6 +45,8 @@ website:
           text: "Regression Decomposition"
         - file: ssc.qmd
           text: "On Small Sample Corrections"
+        - file: quantile-regression.qmd
+          text: "Quantile Regression"
         - text: "Compare fixest & PyFixest"
           file: compare-fixest-pyfixest.qmd
         - text: "Compare Stata & PyFixest"

docs/_sidebar.yml

@@ -6,6 +6,7 @@ website:
       - reference/estimation.estimation.feols.qmd
       - reference/estimation.estimation.fepois.qmd
       - reference/estimation.estimation.feglm.qmd
+      - reference/estimation.estimation.quantreg.qmd
       - reference/did.estimation.did2s.qmd
       - reference/did.estimation.lpdid.qmd
       - reference/did.estimation.event_study.qmd
@@ -21,6 +22,7 @@ website:
       - reference/estimation.feprobit_.Feprobit.qmd
       - reference/estimation.fegaussian_.Fegaussian.qmd
       - reference/estimation.feols_compressed_.FeolsCompressed.qmd
+      - reference/estimation.quantreg.quantreg_.qmd
       section: Estimation Classes
     - contents:
       - reference/did.visualize.panelview.qmd
@@ -29,6 +31,7 @@ website:
       - reference/report.dtable.qmd
       - reference/report.coefplot.qmd
       - reference/report.iplot.qmd
+      - reference/report.qplot.qmd
       - reference/did.visualize.panelview.qmd
       section: Summarize and Visualize
     - contents:

docs/quantile-regression.qmd

@@ -0,0 +1,51 @@
+---
+title: "Quantile Regression"
+format:
+  html:
+    html-table-processing: none
+toc: true
+toc-title: "On this page"
+toc-location: left
+---
+
+PyFixest now experimentally supports quantile regression!
+
+```{python}
+%load_ext autoreload
+
+import pyfixest as pf
+import statsmodels.formula.api as smf
+import numpy as np
+
+data = pf.get_data()
+```
+
+## Basic Example
+
+Just as in `statsmodels`, the function that runs a quantile regression is `quantreg()`.
+
+Below, we loop over 10 different quantiles.
+
+```{python}
+%%capture
+fits = [pf.quantreg("Y ~ X1 + X2 + f1", data = data, quantile = q) for q in np.arange(0.1, 1, 0.1)]
+```
+
+We can inspect the quantile regression results using the dedicated `qplot()` function.
+
+```{python}
+pf.qplot(fits, nrow = 2)
+```
+
+We observe some heterogeneity in the Intercept, but all other variants are homogeneous across users.
+
+## Inference
+
+`pf.quantreg` supports heteroskedasticity-robust inference ("nid") and cluster robust inference following
+Parente & Santos Silva. See this [slide set](https://www.stata.com/meeting/uk15/abstracts/materials/uk15_santossilva.pdf)
+or the [Journal of Econometrics paper](https://repository.essex.ac.uk/8976/1/dp728.pdf) for details.
+
+```{python}
+fit_nid = pf.quantreg("Y ~ X1 + X2 + f1", data = data, quantile = 0.5, vcov = "nid")
+fit_crv = pf.quantreg("Y ~ X1 + X2 + f1", data = data, quantile = 0.5, vcov = {"CRV1": "f1"})
+```

docs/quarto_example/QuartoExample.qmd

@@ -35,16 +35,13 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod
 #| output: false
 import  pandas as pd
 import pyfixest as pf
-
 data = pf.get_data()
-
 fit1 = pf.feols("Y ~ X1 + X2 | f1", data = data)
 fit2 = pf.feols("Y ~ X1 + X2 | f1 + f2", data = data)
 fit3 = pf.feols("Y ~ X1 *X2 | f1 + f2", data = data)
 fit4 = pf.feols("Y2 ~ X1 + X2 | f1", data = data)
 fit5 = pf.feols("Y2 ~ X1 + X2 | f1 + f2", data = data)
 fit6 = pf.feols("Y2 ~ X1 *X2 | f1 + f2", data = data)
-
 labels={
     "Y": "Wage",
     "Y2": "Wealth",
@@ -53,7 +50,6 @@ labels={
     "f1": "Industry",
     "f2": "Year"
 }
-
 ```
 
 # A PyFixest Regression Table
@@ -65,12 +61,9 @@ As @tbl-main shows, LaTex Tables generated by PyFixest can be easily integrated
 #| output: asis
 #| echo: false
 #| tbl-pos: H
-
 mynotes="Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
-
 tab=pf.etable([fit1, fit2, fit4, fit5],
             labels=labels, notes=mynotes, type="tex",
             model_heads=["US", "China", "US", "China"])
-
 print(tab)
-```
+```