Simple wrapper for PyHDFE
Project description
regPyHDFE
To do:
Okay, so, is it true that regHDFE is just linear regression with fixed effects.
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(1) (2) (3)
price price price
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weight 5.741*** 5.703*** 5.741***
(5.44) (5.17) (5.44)
Length Con~s Yes Yes Yes
Turn FE Yes Yes Yes
foreign Yes No Yes
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N 74 74 74
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t statistics in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001
(dropped 884 singleton observations)
(MWFE estimator converged in 8 iterations)
HDFE Linear regression Number of obs = 12,568
Absorbing 2 HDFE groups F( 1, 9454) = 65.21
Prob > F = 0.0000
R-squared = 0.5333
Adj R-squared = 0.3796
Within R-sq. = 0.0069
Root MSE = 0.3351
------------------------------------------------------------------------------
hours_log | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
union | .0922909 .0114286 8.08 0.000 .0698884 .1146934
_cons | 3.505433 .0039948 877.49 0.000 3.497602 3.513264
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
year | 12 0 12 |
idcode | 3102 1 3101 |
-----------------------------------------------------+
. reghdfe hours_log union, absorb(year idcode) keepsingletons
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 8 iterations)
HDFE Linear regression Number of obs = 13,452
Absorbing 2 HDFE groups F( 1, 9454) = 65.21
Prob > F = 0.0000
R-squared = 0.5754
Adj R-squared = 0.3959
Within R-sq. = 0.0069
Root MSE = 0.3351
------------------------------------------------------------------------------
hours_log | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
union | .0922909 .0114286 8.08 0.000 .0698884 .1146934
_cons | 3.502792 .0038949 899.33 0.000 3.495157 3.510427
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
year | 12 0 12 |
idcode | 3986 1 3985 |
-----------------------------------------------------+
Two things they'd like are (1) Weighting and (2) Clustering of Standard Errors
What the hell is weighting:
Let's see if the solution for week 3 homework has anything on it
https://www.stata.com/support/faqs/statistics/analytical-weights-with-linear-regression/
This seems to contain everything on weights
Looks like weighting might be something We can just apply to the design matrix before anything is done, e.g.
for frequency weighting You just duplicate rows.
What the hell is clustering of standard errors:
'kay, so, the idea is that You have categorical variables - prices of massages in different cities for instance.
The idea is that homoscedasticity is violated here - the errors terms may be homo within the group of each city, but hetero between different cities.
Apparently the estimator is still consistent given this wackery, but the estimate for standard errors of coefficients is wrong given this assumption. Clustering accounts for this.
Get a regression going
We want a dataset that is accessible in both stata and python
Okay so We've tried a bunch of stuff and pyhdfe seems to be the most promising.
Intercepts not matching
So We have from stata:
. reghdfe ln_wage hours_log, keepsingletons absorb(year)
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 1 iterations)
HDFE Linear regression Number of obs = 13,452
Absorbing 1 HDFE group F( 1, 13439) = 69.89
Prob > F = 0.0000
R-squared = 0.0469
Adj R-squared = 0.0460
Within R-sq. = 0.0052
Root MSE = 0.4488
------------------------------------------------------------------------------
ln_wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hours_log | .076009 .0090917 8.36 0.000 .058188 .09383
_cons | 1.445715 .032271 44.80 0.000 1.382459 1.508971
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
year | 12 0 12 |
-----------------------------------------------------+
and from pyhdfe
ln_wage ~ hours_log, absorb(year)
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.005
Model: OLS Adj. R-squared: 0.005
Method: Least Squares F-statistic: 69.95
Date: Sun, 06 Dec 2020 Prob (F-statistic): 6.67e-17
Time: 12:06:36 Log-Likelihood: -8305.1
No. Observations: 13452 AIC: 1.661e+04
Df Residuals: 13450 BIC: 1.663e+04
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
x1 0.0760 0.009 8.364 0.000 0.058 0.094
const 1.735e-17 0.004 4.48e-15 1.000 -0.008 0.008
==============================================================================
Omnibus: 404.490 Durbin-Watson: 1.005
Prob(Omnibus): 0.000 Jarque-Bera (JB): 916.777
Skew: 0.158 Prob(JB): 8.40e-200
Kurtosis: 4.239 Cond. No. 2.35
==============================================================================
So things are looking good aside from this cons and const thing. As a rando try, let's try absorbing two things:
. reghdfe ln_wage hours_log, keepsingletons absorb(year idcode)
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 9 iterations)
HDFE Linear regression Number of obs = 13,452
Absorbing 2 HDFE groups F( 1, 9454) = 0.50
Prob > F = 0.4792
R-squared = 0.7564
Adj R-squared = 0.6534
Within R-sq. = 0.0001
Root MSE = 0.2705
------------------------------------------------------------------------------
ln_wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hours_log | -.0058555 .0082759 -0.71 0.479 -.022078 .010367
_cons | 1.734197 .0292564 59.28 0.000 1.676848 1.791545
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
year | 12 0 12 |
idcode | 3986 1 3985 |
-----------------------------------------------------+
ln_wage ~ hours_log, absorb(idcode, year)
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.000
Model: OLS Adj. R-squared: -0.000
Method: Least Squares F-statistic: 0.6654
Date: Sun, 06 Dec 2020 Prob (F-statistic): 0.415
Time: 12:24:58 Log-Likelihood: 386.59
No. Observations: 12568 AIC: -769.2
Df Residuals: 12566 BIC: -754.3
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
x1 -0.0059 0.007 -0.816 0.415 -0.020 0.008
const -1.172e-18 0.002 -5.6e-16 1.000 -0.004 0.004
==============================================================================
Omnibus: 1617.122 Durbin-Watson: 2.143
Prob(Omnibus): 0.000 Jarque-Bera (JB): 16984.817
Skew: -0.215 Prob(JB): 0.00
Kurtosis: 8.679 Cond. No. 3.43
==============================================================================
Okay well the coefficients seem to be working out. Should try another dataset.
Another dataset:
What dataset, though
Three datasets added.
Manually calculate significance in order to be able to adjust it
Okay so there is this results statsmodels.regression.linear_model.RegressionResultsWrapper and maybe I can just manually edit the parameters?
Well, first let's check the the residual sum of squares is the same
Okay so We need to figure out a way to compute redundant degrees of freedom. A suspicion is that when partitioning based on features to be absorbed on, if they end up singletons or have an identical partition, then they are redundant.
Let's start with the singletons.
Okay so We have degrees of freedom.
Clustering
So a problem is that if We absorb and cluster the same variable, things work, and if We absorb x and cluster y then We get a mismatch.
Okay Stata is a fucking joke and an absurdity. Jesus H. Christ.
So, no absorption and clustering on single variable is identical Clustering on two still super close, to the point where it might work Clustering on three is just a failure and won't be delivered.
Absorbing year and clustering on idcode works
I think everything works close enough.
Weighting
. range weights 1 13452
. reghdfe wks_work ttl_exp [fweight=weights], noabsorb
(MWFE estimator converged in 1 iterations)
HDFE Linear regression Number of obs = 90,484,878
Absorbing 1 HDFE group F( 1,90484876)= 2.75e+07
Prob > F = 0.0000
R-squared = 0.2330
Adj R-squared = 0.2330
Within R-sq. = 0.2330
Root MSE = 18.8790
------------------------------------------------------------------------------
wks_work | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ttl_exp | 2.348477 .000448 5242.18 0.000 2.347599 2.349355
_cons | 34.61035 .0036131 9579.02 0.000 34.60327 34.61743
------------------------------------------------------------------------------
okay so We're gonna try to emulate the F scores for GLM lol.
We need SSR - some of squared residuals I think. They are whitened though, idk what that means. Just squared lol.
Okay - the standard results just returns
return self.mse_model/self.mse_resid
mse_model is simple = explained sum of squares / df.model
explained sum of squares is just SSE with mean or whatever
mse_resid is self.ssr/self.df_resid
self.ssr is sum of squared residuals
Well We are having trouble just extracting predictions from the GLM model.
Nevermind. Okay.
Cleaning Up
Cleaner interface
Ah shit, the clustering is just a touch off.
Clustering sanity checks
Are annoying.
reghdfe ttl_exp wks_ue tenure, absorb(year idcode) cluster(idcode)
(dropped 884 singleton observations)
(MWFE estimator converged in 8 iterations)
HDFE Linear regression Number of obs = 12,568
Absorbing 2 HDFE groups F( 2, 3101) = 481.77
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.9459
Adj R-squared = 0.9281
Within R-sq. = 0.2658
Number of clusters (idcode) = 3,102 Root MSE = 1.1693
(Std. Err. adjusted for 3,102 clusters in idcode)
------------------------------------------------------------------------------
| Robust
ttl_exp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
wks_ue | .0040268 .0020563 1.96 0.050 -5.17e-06 .0080587
tenure | .2779749 .0089617 31.02 0.000 .2604034 .2955464
_cons | 5.848487 .0321137 182.12 0.000 5.785521 5.911453
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
year | 12 0 12 |
idcode | 3102 3102 0 *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
and python gives
12568
3102
OLS Regression Results
=======================================================================================
Dep. Variable: ttl_exp R-squared (uncentered): 0.266
Model: OLS Adj. R-squared (uncentered): -1.977
Method: Least Squares F-statistic: 482.2
Date: Sun, 03 Jan 2021 Prob (F-statistic): 4.45e-183
Time: 12:16:31 Log-Likelihood: -18009.
No. Observations: 12568 AIC: 3.602e+04
Df Residuals: 3100 BIC: 3.604e+04
Df Model: 2
Covariance Type: cluster
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
wks_ue 0.0040 0.002 1.959 0.050 -1.67e-06 0.008
tenure 0.2780 0.009 31.033 0.000 0.260 0.296
==============================================================================
Omnibus: 1286.365 Durbin-Watson: 1.788
Prob(Omnibus): 0.000 Jarque-Bera (JB): 8023.661
Skew: 0.285 Prob(JB): 0.00
Kurtosis: 6.873 Cond. No. 2.47
==============================================================================
Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors are robust to cluster correlation (cluster)
params
wks_ue 0.004027
tenure 0.277975
Also checked the residuals and they are identical.
Honestly seems like numerical error.
Comment Code
Checking clustering yet again
Okay so there appears to be an
algo._groups_listobject.
Stata Commands
use data/cleaned_nlswork.dta gen hours_log = log(hours) reghdfe ln_wage hours_log, absorb(idcode year) residuals(res) list res
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