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<td>PyFixest’s <code>quantreg()</code> implementation is tested against R’s quantreg package (by Roger Koenker) for coefficient and NID standard error equivalence</td>
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<td>PyFixest’s <code>quantreg()</code> implementation is tested against R’s quantreg package (by Roger Koenker) for coefficient and NID standard error equivalence.</td>
<td>PyFixest’s local-projections DID estimator is highly influenced by Alex Cardazzi’s R code (published under MIT) for the lpdid package. We also test against the R implementation</td>
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<td>PyFixest’s local-projections DID estimator is highly influenced by Alex Cardazzi’s R code (published under MIT) for the lpdid package. We also test against Alex’ package.</td>
<td>Pailanir & Clarke. PyFixest’s CCV implementation (Abadie et al., QJE 2023) is tested against Daniel Pailanir and Damian Clarke’s Stata implementation. Test data is loaded from Stata <code>.dta</code> files</td>
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<td>Pailanir & Clarke. PyFixest’s CCV implementation (Abadie et al., QJE 2023) is tested against Daniel Pailanir and Damian Clarke’s Stata implementation.</td>
<td>PyFixest’s demeaning results are tested against pyhdfe to ensure equivalence. <code>pyfixest</code>’s first MVP was built using <code>pyhdfe</code> it ran its demeaning algorithm via <code>pyhdfe</code> MAP algo.</td>
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<td>PyFixest’s demeaning results are tested against Jeff Gortmaker’s <code>pyhdfe</code>. <code>pyfixest</code>’s first MVP was built using <code>pyhdfe</code> it ran its demeaning algorithm via <code>pyhdfe</code> MAP algo.</td>
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