Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Revert "update readme" #572

Merged
merged 1 commit into from
Jul 31, 2024
Merged

Revert "update readme" #572

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
315 changes: 34 additions & 281 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,11 +19,10 @@ Nevertheless, for a quick introduction, you can take a look at the [documentatio

## Features

- **OLS**, **WLS** and **IV** Regression (including IV diagnostics)
- **OLS**, **WLS** and **IV** Regression
- **Poisson Regression** following the [pplmhdfe algorithm](https://journals.sagepub.com/doi/full/10.1177/1536867X20909691)
- Multiple Estimation Syntax
- Several **Robust** and **Cluster Robust Variance-Covariance** Estimators
- Wald Tests of Multiple Simultaneous Hypotheses
- **Wild Cluster Bootstrap** Inference (via
[wildboottest](https://github.com/py-econometrics/wildboottest))
- **Difference-in-Differences** Estimators:
Expand Down Expand Up @@ -72,76 +71,6 @@ data = pf.get_data()
pf.feols("Y ~ X1 | f1 + f2", data=data).summary()
```



<div id="W5xl50"></div>
<script type="text/javascript" data-lets-plot-script="library">
if(!window.letsPlotCallQueue) {
window.letsPlotCallQueue = [];
};
window.letsPlotCall = function(f) {
window.letsPlotCallQueue.push(f);
};
(function() {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://cdn.jsdelivr.net/gh/JetBrains/[email protected]/js-package/distr/lets-plot.min.js";
script.onload = function() {
window.letsPlotCall = function(f) {f();};
window.letsPlotCallQueue.forEach(function(f) {f();});
window.letsPlotCallQueue = [];

};
script.onerror = function(event) {
window.letsPlotCall = function(f) {}; // noop
window.letsPlotCallQueue = [];
var div = document.createElement("div");
div.style.color = 'darkred';
div.textContent = 'Error loading Lets-Plot JS';
document.getElementById("W5xl50").appendChild(div);
};
var e = document.getElementById("W5xl50");
e.appendChild(script);
})()
</script>





<div id="9eFgOT"></div>
<script type="text/javascript" data-lets-plot-script="library">
if(!window.letsPlotCallQueue) {
window.letsPlotCallQueue = [];
};
window.letsPlotCall = function(f) {
window.letsPlotCallQueue.push(f);
};
(function() {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://cdn.jsdelivr.net/gh/JetBrains/[email protected]/js-package/distr/lets-plot.min.js";
script.onload = function() {
window.letsPlotCall = function(f) {f();};
window.letsPlotCallQueue.forEach(function(f) {f();});
window.letsPlotCallQueue = [];

};
script.onerror = function(event) {
window.letsPlotCall = function(f) {}; // noop
window.letsPlotCallQueue = [];
var div = document.createElement("div");
div.style.color = 'darkred';
div.textContent = 'Error loading Lets-Plot JS';
document.getElementById("9eFgOT").appendChild(div);
};
var e = document.getElementById("9eFgOT");
e.appendChild(script);
})()
</script>



###

Estimation: OLS
Expand All @@ -153,7 +82,7 @@ pf.feols("Y ~ X1 | f1 + f2", data=data).summary()
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1 | -0.919 | 0.065 | -14.057 | 0.000 | -1.053 | -0.786 |
---
RMSE: 1.441 R2: 0.609 R2 Within: 0.2
RMSE: 1.441 R2: 0.609 R2 Within: 0.2


### Multiple Estimation
Expand All @@ -167,131 +96,26 @@ syntax](https://aeturrell.github.io/coding-for-economists/econmt-regression.html
# OLS Estimation: estimate multiple models at once
fit = pf.feols("Y + Y2 ~X1 | csw0(f1, f2)", data = data, vcov = {'CRV1':'group_id'})
# Print the results
pf.etable(fit.to_list())
fit.etable()
```




<style type="text/css">
#T_6bd5a tbody tr:nth-child(0) td {
background-color: #f0f0f0;
}
#T_6bd5a tbody tr:nth-child(1) td {
border-bottom: 2px solid black;
}
#T_6bd5a tbody tr:nth-child(3) td {
border-bottom: 1px solid black;
}
#T_6bd5a tbody tr:nth-child(5) td {
border-bottom: 1px solid black;
}
#T_6bd5a tbody tr:nth-child(8) td {
border-bottom: 1px solid black;
}
#T_6bd5a tbody td {
background-color: #ffffff;
}
#T_6bd5a tbody tr td:first-child {
background-color: #f0f0f0;
font-weight: bold;
text-align: left;
}
#T_6bd5a_row0_col0, #T_6bd5a_row0_col1, #T_6bd5a_row0_col2, #T_6bd5a_row0_col3, #T_6bd5a_row0_col4, #T_6bd5a_row0_col5, #T_6bd5a_row0_col6, #T_6bd5a_row1_col0, #T_6bd5a_row1_col1, #T_6bd5a_row1_col2, #T_6bd5a_row1_col3, #T_6bd5a_row1_col4, #T_6bd5a_row1_col5, #T_6bd5a_row1_col6, #T_6bd5a_row2_col0, #T_6bd5a_row2_col1, #T_6bd5a_row2_col2, #T_6bd5a_row2_col3, #T_6bd5a_row2_col4, #T_6bd5a_row2_col5, #T_6bd5a_row2_col6, #T_6bd5a_row3_col0, #T_6bd5a_row3_col1, #T_6bd5a_row3_col2, #T_6bd5a_row3_col3, #T_6bd5a_row3_col4, #T_6bd5a_row3_col5, #T_6bd5a_row3_col6, #T_6bd5a_row4_col0, #T_6bd5a_row4_col1, #T_6bd5a_row4_col2, #T_6bd5a_row4_col3, #T_6bd5a_row4_col4, #T_6bd5a_row4_col5, #T_6bd5a_row4_col6, #T_6bd5a_row5_col0, #T_6bd5a_row5_col1, #T_6bd5a_row5_col2, #T_6bd5a_row5_col3, #T_6bd5a_row5_col4, #T_6bd5a_row5_col5, #T_6bd5a_row5_col6, #T_6bd5a_row6_col0, #T_6bd5a_row6_col1, #T_6bd5a_row6_col2, #T_6bd5a_row6_col3, #T_6bd5a_row6_col4, #T_6bd5a_row6_col5, #T_6bd5a_row6_col6, #T_6bd5a_row7_col0, #T_6bd5a_row7_col1, #T_6bd5a_row7_col2, #T_6bd5a_row7_col3, #T_6bd5a_row7_col4, #T_6bd5a_row7_col5, #T_6bd5a_row7_col6 {
text-align: right;
}
</style>
<table id="T_6bd5a">
<caption>Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001. Format of coefficient cell:
Coefficient (Std. Error)</caption>
<thead>
<tr>
<th id="T_6bd5a_level0_col0" class="col_heading level0 col0" ></th>
<th id="T_6bd5a_level0_col1" class="col_heading level0 col1" >est1</th>
<th id="T_6bd5a_level0_col2" class="col_heading level0 col2" >est2</th>
<th id="T_6bd5a_level0_col3" class="col_heading level0 col3" >est3</th>
<th id="T_6bd5a_level0_col4" class="col_heading level0 col4" >est4</th>
<th id="T_6bd5a_level0_col5" class="col_heading level0 col5" >est5</th>
<th id="T_6bd5a_level0_col6" class="col_heading level0 col6" >est6</th>
</tr>
</thead>
<tbody>
<tr>
<td id="T_6bd5a_row0_col0" class="data row0 col0" >depvar</td>
<td id="T_6bd5a_row0_col1" class="data row0 col1" >Y</td>
<td id="T_6bd5a_row0_col2" class="data row0 col2" >Y2</td>
<td id="T_6bd5a_row0_col3" class="data row0 col3" >Y</td>
<td id="T_6bd5a_row0_col4" class="data row0 col4" >Y2</td>
<td id="T_6bd5a_row0_col5" class="data row0 col5" >Y</td>
<td id="T_6bd5a_row0_col6" class="data row0 col6" >Y2</td>
</tr>
<tr>
<td id="T_6bd5a_row1_col0" class="data row1 col0" >Intercept</td>
<td id="T_6bd5a_row1_col1" class="data row1 col1" >0.919*** (0.121)</td>
<td id="T_6bd5a_row1_col2" class="data row1 col2" >1.064*** (0.232)</td>
<td id="T_6bd5a_row1_col3" class="data row1 col3" ></td>
<td id="T_6bd5a_row1_col4" class="data row1 col4" ></td>
<td id="T_6bd5a_row1_col5" class="data row1 col5" ></td>
<td id="T_6bd5a_row1_col6" class="data row1 col6" ></td>
</tr>
<tr>
<td id="T_6bd5a_row2_col0" class="data row2 col0" >X1</td>
<td id="T_6bd5a_row2_col1" class="data row2 col1" >-1.000*** (0.117)</td>
<td id="T_6bd5a_row2_col2" class="data row2 col2" >-1.322*** (0.211)</td>
<td id="T_6bd5a_row2_col3" class="data row2 col3" >-0.949*** (0.087)</td>
<td id="T_6bd5a_row2_col4" class="data row2 col4" >-1.266*** (0.212)</td>
<td id="T_6bd5a_row2_col5" class="data row2 col5" >-0.919*** (0.069)</td>
<td id="T_6bd5a_row2_col6" class="data row2 col6" >-1.228*** (0.194)</td>
</tr>
<tr>
<td id="T_6bd5a_row3_col0" class="data row3 col0" >f2</td>
<td id="T_6bd5a_row3_col1" class="data row3 col1" >-</td>
<td id="T_6bd5a_row3_col2" class="data row3 col2" >-</td>
<td id="T_6bd5a_row3_col3" class="data row3 col3" >-</td>
<td id="T_6bd5a_row3_col4" class="data row3 col4" >-</td>
<td id="T_6bd5a_row3_col5" class="data row3 col5" >x</td>
<td id="T_6bd5a_row3_col6" class="data row3 col6" >x</td>
</tr>
<tr>
<td id="T_6bd5a_row4_col0" class="data row4 col0" >f1</td>
<td id="T_6bd5a_row4_col1" class="data row4 col1" >-</td>
<td id="T_6bd5a_row4_col2" class="data row4 col2" >-</td>
<td id="T_6bd5a_row4_col3" class="data row4 col3" >x</td>
<td id="T_6bd5a_row4_col4" class="data row4 col4" >x</td>
<td id="T_6bd5a_row4_col5" class="data row4 col5" >x</td>
<td id="T_6bd5a_row4_col6" class="data row4 col6" >x</td>
</tr>
<tr>
<td id="T_6bd5a_row5_col0" class="data row5 col0" >R2</td>
<td id="T_6bd5a_row5_col1" class="data row5 col1" >0.123</td>
<td id="T_6bd5a_row5_col2" class="data row5 col2" >0.037</td>
<td id="T_6bd5a_row5_col3" class="data row5 col3" >0.437</td>
<td id="T_6bd5a_row5_col4" class="data row5 col4" >0.115</td>
<td id="T_6bd5a_row5_col5" class="data row5 col5" >0.609</td>
<td id="T_6bd5a_row5_col6" class="data row5 col6" >0.168</td>
</tr>
<tr>
<td id="T_6bd5a_row6_col0" class="data row6 col0" >S.E. type</td>
<td id="T_6bd5a_row6_col1" class="data row6 col1" >by: group_id</td>
<td id="T_6bd5a_row6_col2" class="data row6 col2" >by: group_id</td>
<td id="T_6bd5a_row6_col3" class="data row6 col3" >by: group_id</td>
<td id="T_6bd5a_row6_col4" class="data row6 col4" >by: group_id</td>
<td id="T_6bd5a_row6_col5" class="data row6 col5" >by: group_id</td>
<td id="T_6bd5a_row6_col6" class="data row6 col6" >by: group_id</td>
</tr>
<tr>
<td id="T_6bd5a_row7_col0" class="data row7 col0" >Observations</td>
<td id="T_6bd5a_row7_col1" class="data row7 col1" >998</td>
<td id="T_6bd5a_row7_col2" class="data row7 col2" >999</td>
<td id="T_6bd5a_row7_col3" class="data row7 col3" >997</td>
<td id="T_6bd5a_row7_col4" class="data row7 col4" >998</td>
<td id="T_6bd5a_row7_col5" class="data row7 col5" >997</td>
<td id="T_6bd5a_row7_col6" class="data row7 col6" >998</td>
</tr>
</tbody>
</table>


est1 est2 est3 est4 est5 est6
------------ ----------------- ----------------- ----------------- ----------------- ----------------- -----------------
depvar Y Y2 Y Y2 Y Y2
------------------------------------------------------------------------------------------------------------------------------
Intercept 0.919*** (0.121) 1.064*** (0.232)
X1 -1.000*** (0.117) -1.322*** (0.211) -0.949*** (0.087) -1.266*** (0.212) -0.919*** (0.069) -1.228*** (0.194)
------------------------------------------------------------------------------------------------------------------------------
f2 - - - - x x
f1 - - x x x x
------------------------------------------------------------------------------------------------------------------------------
R2 0.123 0.037 0.437 0.115 0.609 0.168
S.E. type by: group_id by: group_id by: group_id by: group_id by: group_id by: group_id
Observations 998 999 997 998 997 998
------------------------------------------------------------------------------------------------------------------------------
Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001
Format of coefficient cell:
Coefficient (Std. Error)



Expand All @@ -310,16 +134,16 @@ fit1.vcov("hetero").summary()
###

Estimation: OLS
Dep. var.: Y, Fixed effects:
Dep. var.: Y
Inference: hetero
Observations: 998

| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5% | 97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| Intercept | 0.919 | 0.112 | 8.227 | 0.000 | 0.699 | 1.138 |
| X1 | -1.000 | 0.082 | -12.140 | 0.000 | -1.162 | -0.838 |
| Intercept | 0.919 | 0.112 | 8.223 | 0.000 | 0.699 | 1.138 |
| X1 | -1.000 | 0.082 | -12.134 | 0.000 | -1.162 | -0.838 |
---
RMSE: 2.158 R2: 0.123
RMSE: 2.158 R2: 0.123


### Poisson Regression via `fepois()`
Expand Down Expand Up @@ -355,91 +179,20 @@ syntax:

```python
fit_iv = pf.feols("Y ~ 1 | f1 | X1 ~ Z1", data = data)
pf.etable([fit_iv._model_1st_stage, fit_iv])
fit_iv.summary()
```

###

Estimation: IV
Dep. var.: Y, Fixed effects: f1
Inference: CRV1
Observations: 997


<style type="text/css">
#T_52718 tbody tr:nth-child(0) td {
background-color: #f0f0f0;
}
#T_52718 tbody tr:nth-child(1) td {
border-bottom: 2px solid black;
}
#T_52718 tbody tr:nth-child(3) td {
border-bottom: 1px solid black;
}
#T_52718 tbody tr:nth-child(4) td {
border-bottom: 1px solid black;
}
#T_52718 tbody tr:nth-child(7) td {
border-bottom: 1px solid black;
}
#T_52718 tbody td {
background-color: #ffffff;
}
#T_52718 tbody tr td:first-child {
background-color: #f0f0f0;
font-weight: bold;
text-align: left;
}
#T_52718_row0_col0, #T_52718_row0_col1, #T_52718_row0_col2, #T_52718_row1_col0, #T_52718_row1_col1, #T_52718_row1_col2, #T_52718_row2_col0, #T_52718_row2_col1, #T_52718_row2_col2, #T_52718_row3_col0, #T_52718_row3_col1, #T_52718_row3_col2, #T_52718_row4_col0, #T_52718_row4_col1, #T_52718_row4_col2, #T_52718_row5_col0, #T_52718_row5_col1, #T_52718_row5_col2, #T_52718_row6_col0, #T_52718_row6_col1, #T_52718_row6_col2 {
text-align: right;
}
</style>
<table id="T_52718">
<caption>Significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001. Format of coefficient cell:
Coefficient (Std. Error)</caption>
<thead>
<tr>
<th id="T_52718_level0_col0" class="col_heading level0 col0" ></th>
<th id="T_52718_level0_col1" class="col_heading level0 col1" >est1</th>
<th id="T_52718_level0_col2" class="col_heading level0 col2" >est2</th>
</tr>
</thead>
<tbody>
<tr>
<td id="T_52718_row0_col0" class="data row0 col0" >depvar</td>
<td id="T_52718_row0_col1" class="data row0 col1" >Y</td>
<td id="T_52718_row0_col2" class="data row0 col2" >Y</td>
</tr>
<tr>
<td id="T_52718_row1_col0" class="data row1 col0" >Z1</td>
<td id="T_52718_row1_col1" class="data row1 col1" >0.396*** (0.014)</td>
<td id="T_52718_row1_col2" class="data row1 col2" ></td>
</tr>
<tr>
<td id="T_52718_row2_col0" class="data row2 col0" >X1</td>
<td id="T_52718_row2_col1" class="data row2 col1" ></td>
<td id="T_52718_row2_col2" class="data row2 col2" >-1.025*** (0.115)</td>
</tr>
<tr>
<td id="T_52718_row3_col0" class="data row3 col0" >f1</td>
<td id="T_52718_row3_col1" class="data row3 col1" >x</td>
<td id="T_52718_row3_col2" class="data row3 col2" >x</td>
</tr>
<tr>
<td id="T_52718_row4_col0" class="data row4 col0" >R2</td>
<td id="T_52718_row4_col1" class="data row4 col1" >0.419</td>
<td id="T_52718_row4_col2" class="data row4 col2" >-</td>
</tr>
<tr>
<td id="T_52718_row5_col0" class="data row5 col0" >S.E. type</td>
<td id="T_52718_row5_col1" class="data row5 col1" >by: f1</td>
<td id="T_52718_row5_col2" class="data row5 col2" >by: f1</td>
</tr>
<tr>
<td id="T_52718_row6_col0" class="data row6 col0" >Observations</td>
<td id="T_52718_row6_col1" class="data row6 col1" >994</td>
<td id="T_52718_row6_col2" class="data row6 col2" >997</td>
</tr>
</tbody>
</table>



| Coefficient | Estimate | Std. Error | t value | Pr(>|t|) | 2.5% | 97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1 | -1.025 | 0.115 | -8.930 | 0.000 | -1.259 | -0.790 |
---

## Call for Contributions

Expand Down
Loading
Loading