25. Weak IV Experiments#

25.1. A Simple Example of Properties of IV estimator when Instruments are Weak#

Simulation Design

import hdmpy
import numpy as np
import random
import statsmodels.api as sm
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from statsmodels.sandbox.regression.gmm import IV2SLS
import numpy as np                                                              
import seaborn as sns                                                           
from scipy import stats                                                         
import matplotlib.pyplot as plt 
# Simulation Design

# Set seed
np.random.seed(0)
B = 1000
IVEst = np.zeros( B )
n = 100
beta = .25

mean = 0
sd = 1

U = np.random.normal( mean , sd, n ).reshape( n, 1 )
Z = np.random.normal( mean , sd, n ).reshape( n, 1 )
D = beta*Z + U 
Y = D + U

mod = sm.OLS(D, sm.add_constant(Z))    # Describe model
res = mod.fit()
print(res.summary())
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.121
Model:                            OLS   Adj. R-squared:                  0.112
Method:                 Least Squares   F-statistic:                     13.47
Date:                Wed, 19 May 2021   Prob (F-statistic):           0.000395
Time:                        07:45:45   Log-Likelihood:                -142.05
No. Observations:                 100   AIC:                             288.1
Df Residuals:                      98   BIC:                             293.3
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.0509      0.101      0.501      0.617      -0.151       0.252
x1             0.3588      0.098      3.670      0.000       0.165       0.553
==============================================================================
Omnibus:                        0.445   Durbin-Watson:                   1.865
Prob(Omnibus):                  0.801   Jarque-Bera (JB):                0.594
Skew:                           0.041   Prob(JB):                        0.743
Kurtosis:                       2.632   Cond. No.                         1.09
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
IV = IV2SLS(Y, D, sm.add_constant(Z))
IV_res = IV.fit()
print(IV_res.summary())
                          IV2SLS Regression Results                           
==============================================================================
Dep. Variable:                      y   R-squared:                       0.892
Model:                         IV2SLS   Adj. R-squared:                  0.891
Method:                     Two Stage   F-statistic:                       nan
                        Least Squares   Prob (F-statistic):                nan
Date:                Wed, 19 May 2021                                         
Time:                        07:45:45                                         
No. Observations:                 100                                         
Df Residuals:                      99                                         
Df Model:                           1                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1             1.3230      0.180      7.365      0.000       0.967       1.679
==============================================================================
Omnibus:                        0.446   Durbin-Watson:                   1.864
Prob(Omnibus):                  0.800   Jarque-Bera (JB):                0.595
Skew:                           0.044   Prob(JB):                        0.743
Kurtosis:                       2.633   Cond. No.                         1.00
==============================================================================
IV_res.summary2().tables[1]["Coef."][0]
1.3230345755631345

Note that the instrument is weak here (contolled by \(\beta\)) – the t-stat is less than 4.

25.2. Run 1000 trials to evaluate distribution of the IV estimator#

# Simulation design 

# Set seed
np.random.seed(0)
B = 1000 # Trials
IVEst = np.zeros( B )

for i in range( 0, B ):
    U = np.random.normal( mean , sd, n ).reshape( n, 1 )
    Z = np.random.normal( mean , sd, n ).reshape( n, 1 )
    D = beta*Z + U 
    Y = D + U
    
    IV = IV2SLS(Y, D, sm.add_constant(Z))
    IV_res = IV.fit()
    
    IVEst[ i ] = IV_res.summary2().tables[1]["Coef."][0]
IVEst
array([ 1.32303458e+00,  1.35920545e+00,  1.27528177e+00,  1.31795707e+00,
        8.92612860e-01,  1.15075743e+00,  1.39541223e+00,  1.24046187e+00,
        1.03656449e+00,  7.49148368e-01,  1.66467612e-01,  5.42772305e-01,
       -2.49194696e+00,  2.28867549e-01,  8.84861973e-02,  1.33344096e+00,
        1.27515523e+00,  2.73053783e-01,  1.13439410e+00,  9.08455140e-01,
        1.33805748e+00,  1.15216886e+00,  1.22138349e+00,  1.11055465e+00,
        7.70050438e-01,  1.46744095e+00,  5.09039625e-01,  9.89986241e-01,
        1.33663526e+00,  9.24970759e-01,  1.18487116e+00,  9.37873503e-01,
        1.13082697e+00,  4.69246357e-01,  1.10240992e+00,  1.29446004e+00,
        4.58298687e-01,  1.18565568e+00,  7.44442492e-01,  1.17511530e+00,
        1.27094553e+00,  1.24699451e+00,  1.61650625e+00,  8.75531382e-01,
        2.43879171e-01,  1.35811277e+00,  1.30501832e+00,  9.55013337e-01,
        1.01032459e+00,  1.07437955e+00,  1.22472863e+00,  8.60868688e-01,
        1.21146144e+00,  7.32039813e-01,  9.40206713e-01,  1.41148446e+00,
        1.29966760e+00,  1.26453593e+00,  9.13812915e-01,  9.34567612e-01,
        6.84297515e-01,  1.40724596e+00,  1.28151344e+00,  1.38985953e+00,
        1.29113102e+00,  1.15739705e+00,  1.14614453e+00,  2.87099292e-01,
        4.57912733e-01,  1.03245568e+00,  1.10229414e+00,  1.58310700e+00,
        1.24944984e+00,  1.24387062e+00,  1.32527584e+00,  3.63357240e-01,
        1.18653434e+00,  1.11714173e+00,  1.13778936e+00,  3.39613558e+00,
        1.48160717e+00,  1.17381107e+00,  1.29739942e+00,  1.44539670e+00,
        1.67834496e-01,  4.29858841e-01,  1.52581412e+00,  1.03527227e+00,
        1.21354343e+00, -3.02276271e-01,  1.42109579e+00,  1.22173292e+00,
        1.49972061e+00,  1.26783374e+00,  2.58161595e+00,  1.33147950e+00,
        1.43668405e+00,  1.03611696e+00,  1.02444094e+00,  1.43756447e+00,
        1.12728877e+00,  1.20267145e+00,  8.00063086e-01,  1.44766154e+00,
        1.53081600e+00,  1.31222239e+00,  1.39818567e+00,  4.83095115e-01,
        1.24265612e+00,  1.35660283e+00,  1.04598942e-01,  2.09486115e-01,
        1.37862550e+00,  4.22518713e-01,  5.37759170e+00,  1.18962915e+00,
        1.03492375e+00,  1.32032934e+00,  1.51287197e+00,  1.62947558e+00,
       -1.99980289e-02,  1.24786633e+00,  1.14443476e+00,  1.33176297e+00,
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        9.38483134e-01,  9.56070266e-01,  1.15539125e+00,  1.33453991e+00,
        9.88640527e-01,  1.19216990e+00,  1.18096849e+00,  4.53840105e-01,
       -4.23748886e-01,  1.11187218e+00,  2.12152894e-01,  3.07240854e-01,
        1.31925716e+00,  1.37409425e+00,  1.33947128e+00,  1.12822274e+00,
        1.18732553e+00,  8.30626316e-01,  1.05142081e+00,  1.56440910e+00,
        1.11986862e+00,  2.92530639e+00,  1.08744721e+00,  9.88174618e-01,
        9.92507285e-01,  6.78037801e-01,  1.37133137e+00,  1.42104251e+00,
        1.38643496e+00,  1.12749699e-01, -5.17347541e-01,  9.95097080e-01,
        5.86376476e-01,  1.14159010e+00,  1.01006229e+00,  1.06947449e+00,
        8.52158527e-01,  1.27289028e+00,  1.54941271e+00,  1.17234957e+00,
        8.90879270e-01,  8.35292185e-01,  3.18218906e-01,  1.32867384e+00,
        1.34630510e+00,  7.19693407e-01,  1.04242749e+00,  1.41108700e+00,
        1.40093726e+00,  5.69295318e-01,  5.28410359e-01,  1.28309037e+00,
        1.13900217e+00,  1.15515212e+00,  1.01446134e+00,  1.29257527e+00,
        1.30130753e+00,  1.31194707e+00,  1.10400148e+00,  1.32288595e+00,
        1.34337237e+00,  1.26188820e+00,  1.53649261e+00,  1.11468620e+00,
        1.20978862e+00,  1.40850800e+00,  9.44082312e-01,  1.10816544e+00,
        5.36224877e-01,  9.27126041e-01,  1.11168413e+00,  1.56231471e+00,
        1.40802831e+00,  1.28452651e+00,  1.52437795e+00, -8.32898324e-01,
        1.35255765e+00,  1.49676911e+00,  1.32018121e+00, -3.21693068e+00,
        9.88946138e-01,  9.18907598e-01,  7.25815881e-01,  1.11012613e+00,
        1.44999517e+00,  1.24624659e+00,  7.47846434e-01,  1.45436287e+00,
        9.21874865e-01,  9.93054865e-01,  1.54262660e+00,  1.36832707e+00,
        1.19945626e+00,  1.25028452e+00,  1.10937451e+00,  8.71109868e-01,
        1.30248136e+00,  1.26278625e+00,  9.89599925e-01,  4.87433256e-01,
        1.31540333e+00,  9.44303140e-01,  1.37829071e+00,  1.32361790e+00,
        9.02691450e-01,  1.45037309e+00,  4.42604489e-01,  1.30533742e+00,
        1.32423103e+00,  8.14894513e-01,  1.52454123e+00,  1.02240305e+00,
        1.38350065e+00, -5.60361299e-02,  1.39753346e+00,  1.03189032e+00,
        2.90338834e-01,  7.35713956e-01,  1.55730296e+00,  1.06104061e+00,
        1.32785753e+00,  1.02937644e+00,  1.43870995e+00,  1.19478499e+00,
        1.47577194e+00,  1.47935857e+00,  1.20386969e+00,  1.39491500e+00,
        7.54434990e-01,  1.18854786e+00,  1.16714769e+00,  6.37590089e-01,
        1.22783361e+00,  1.33281320e+00,  1.12591022e+00,  1.41095271e+00,
        3.86505918e-01,  8.56403909e-01,  9.84028450e-01,  1.33992896e+00,
        6.35742112e-01, -1.76508231e-01,  1.35595715e+00,  7.93231026e-02,
        1.34521126e+00,  1.32753201e+00,  1.08995358e+00,  5.57807506e-01,
        1.37465798e+00,  1.22066850e+00,  1.09502911e+00,  1.16568727e+00,
       -1.88324595e-01,  1.30737880e+00,  9.80379104e-01, -2.24027257e-01,
        1.22881433e+00,  2.20139978e-01,  1.09356435e+00,  1.28259031e+00,
        1.32861799e+00,  9.51159983e-01,  1.19519117e+00,  1.20570451e+00,
        1.46365170e+00,  1.04461984e+00,  1.32591144e+00,  1.37756117e+00,
        1.12730742e+00,  1.28658429e+00,  1.11817271e+00,  1.12178244e+00,
        1.38315702e+00,  1.17152641e+00,  1.39696599e+00,  1.45368854e+00,
        1.34279809e+00,  1.23709543e+00,  1.12178307e+00,  1.04349302e+00,
        7.20758152e-01,  1.40408718e+00,  1.39623268e+00,  1.59440888e+00,
        1.27691531e+00,  1.25228537e+00,  1.11874432e+00, -8.10962817e-01,
        1.22688864e+00,  1.37120024e+00,  1.27250789e+00, -3.03776250e+00,
        7.07734402e-01,  1.34184443e+00,  1.44191701e+00,  1.32428779e+00,
        6.92968414e-01,  1.41684666e+00,  1.21867534e+00,  1.41548339e+00,
        1.07875642e+00,  1.12031667e+00,  1.15682093e+00,  1.02380907e+00,
        1.29790932e+00,  6.10001135e-01,  9.18477825e-01,  8.85177546e-01,
        8.34082813e-01,  9.65266918e-01,  1.14343186e+00,  1.36294114e+00,
        1.30838293e+00,  9.43663980e-01,  1.48502096e+00,  1.16091538e+00,
        3.98285697e-01,  9.83136066e-01,  1.44922913e+00,  1.13483997e+00,
        1.02698326e+00,  1.33467053e+00,  1.19747190e+00,  1.07899534e+00,
        2.04752099e-01,  1.14148383e+00,  8.36933070e-01,  5.76883774e-01,
        1.34417868e+00,  3.86177340e-01,  9.48891993e-01,  1.34230104e+00,
        1.31290351e+00, -8.62463492e-01,  1.33521798e+00,  1.42298987e+00,
        7.95445130e-01,  2.39007909e-01,  9.64954374e-01,  1.33431615e+00,
        7.77471759e-01,  1.19539489e+00,  2.58453768e+00,  1.23924481e+00,
        1.07814755e+00,  1.32449126e+00,  1.42179880e+00,  1.05848146e+00,
        1.12603354e+00,  1.30407261e+00,  5.80324669e-01,  4.42489377e-01,
        6.51252961e-01,  1.12839791e+00,  8.59799478e-01,  1.28927108e+00,
        1.47531890e+00,  6.24837371e-01,  1.36674055e+00,  1.43088976e+00,
        1.31519704e+00,  4.21637814e-01,  1.13566021e+00,  1.51666872e+00,
        1.06944208e+00,  1.31684239e+00,  7.60626307e-01,  1.42676366e+00,
        1.37706045e+00,  1.48144337e+00,  4.36692671e-01,  1.12782758e+00,
        1.56906906e-01,  8.69675139e-01,  1.13830114e+00,  1.02908132e+00,
        6.08507301e-01,  1.36313118e+00,  1.25942438e+00,  1.07933761e+00,
        2.72481723e-02,  1.54245328e+00,  1.47878527e+00,  1.17807279e+00,
        2.37096792e-01,  1.28687570e+00,  1.33872441e+00,  1.17702231e+00,
        1.29424835e+00,  6.52956275e-01,  1.27376371e+00, -4.02844985e-01,
        1.18582479e+00,  7.12641718e-01,  1.18496444e+00,  1.52111698e+00,
        1.56938577e+00,  4.65942681e-01,  1.22030499e+00,  1.10253572e+00,
        1.05440748e+00,  8.55348687e-01,  1.79279142e+00,  1.39551327e-01,
        1.41338404e-02,  9.43247126e-01,  1.31820855e+00,  4.98768321e-01,
        1.21868298e+00, -4.52300008e-01,  1.21129161e+00,  3.84348557e-01,
        1.46537460e+00,  1.20801144e+00,  9.37438344e-01,  1.03776649e+00,
        1.08672913e+00,  1.37299258e+00,  1.21306323e+00,  8.14148969e-01,
        1.27591566e+00,  1.34495619e+00,  1.16755510e+00,  1.08050991e+00,
       -4.65086414e-01,  1.39345024e+00,  1.19866347e+00,  8.76073329e-01,
       -1.61598436e+00,  1.33056599e+00,  1.00548014e+00,  1.25407906e+00,
        9.39805556e-01,  1.25042203e+00,  4.03115484e-01,  1.02555037e+00,
        1.03509362e+00,  1.28286173e+00,  6.66337819e-01,  9.92242894e-01,
        1.25839187e+00,  1.22418821e+00,  1.13508492e+00,  1.03587955e+00,
        1.51709153e+00,  1.31774582e+00,  9.69874214e-01,  9.53412450e-01,
        5.15929376e-01,  1.23296144e+00,  1.50510070e+00,  1.28236055e+00,
        4.79706783e-01,  1.34511152e+00,  4.63752828e-01, -8.45750592e+00,
        7.37367842e-01,  1.13146346e+00,  1.00341974e+00,  1.18455282e+00,
        1.29836583e+00,  1.00890744e+00,  1.33786611e+00,  1.33100738e+00,
        1.25407371e+00,  9.66934407e-01,  1.41766444e+00,  2.72491218e-01,
        1.48531425e+00,  5.26059101e-01,  1.41585286e+00,  1.31854104e+00,
        6.03793153e-01,  1.29952145e+00,  1.38914490e+00,  1.36466327e+00,
        1.12965207e+00,  1.35617001e+00,  1.28085812e+00,  1.09631459e+00,
        9.99429753e-01,  1.01913967e+00,  1.23506267e+00, -5.69507455e-01,
        1.19392072e+00,  7.17487864e-01,  1.24804722e+00,  1.14509059e+00,
        1.10185940e+00,  1.19125004e+00,  1.01756959e+00,  8.64187997e-01,
        1.15136946e+00,  1.07858244e+00,  1.17074625e+00,  1.13275137e+00,
        1.53618005e+00,  8.85346745e-01,  1.49150035e+00,  1.24094570e+00,
        1.34660428e+00,  1.35990651e+00,  1.11749082e+00,  1.40267786e+00,
        1.24015812e+00, -6.42450893e-01,  1.38474364e+00,  1.49039376e+00,
        1.10446377e+00,  1.40844508e+00,  3.21767016e-01,  1.24277968e+00,
        1.09703048e+00,  1.39338370e+00, -1.21395176e+00,  9.80807891e-01,
        1.38080192e+00,  1.41629699e+00,  1.17019274e+00,  1.41991014e+00,
        1.43651502e+00,  1.42841717e+00,  1.00039286e+00,  1.32185031e+00,
        6.77663894e-01,  1.32689302e+00,  1.22726151e+00,  1.34652266e+00,
        9.98460144e-01,  9.66729733e-01,  1.07077393e+00,  1.54065224e+00,
        1.18267795e+00,  1.27304628e+00,  3.52190516e-01,  6.12756813e-01,
        6.22563613e-01,  1.40459119e+00,  1.45819022e+00,  2.13394494e-01,
        1.05855640e+00,  1.75323717e+00,  1.10948438e+00,  1.06383447e+00,
        1.42467340e+00,  1.30593090e+00,  1.17511922e+00,  1.23944569e+00,
        1.25715005e+00,  1.45706328e+00,  2.99587726e-01,  1.02493247e+00,
        4.11723877e-01,  1.12258063e+00,  1.31857757e+00,  1.24714511e+00,
        8.76377113e-01,  1.73947783e-01,  5.93581002e-01,  1.45934585e+00,
        9.91487965e-01,  1.32661726e+00,  9.77014655e-01,  1.39032524e+00,
        1.43143681e+00,  1.13880149e+00,  1.35007980e+00,  1.14084549e+00,
        1.27136208e+00,  2.51704058e-01,  9.63524649e-01,  1.17856431e+00,
        1.54391061e-01,  1.51641147e+00,  1.48794191e+00,  1.13105197e+00,
       -1.05384722e+00,  1.64389636e+00,  1.08539288e+00,  7.18056321e-01,
        1.09766713e+00,  6.79650209e-01,  1.23794219e+00,  1.29919969e+00,
        1.30944601e+00,  1.27441944e+00,  1.02418288e+00,  9.05084541e-01,
       -1.02635907e+00,  1.26823122e+00,  9.09081373e-01,  1.34034246e+00,
        1.36595293e+00,  1.50841765e+00,  1.12491908e+00,  7.17902290e-01,
        7.32765580e-01,  1.30007759e+00,  4.16058259e-01,  5.86671685e-01,
        1.12035419e+00,  2.54290052e-01,  1.26243848e+00,  1.42333571e+00,
        3.51647851e-01,  1.05530652e+00,  1.54981041e+00,  1.23492587e+00,
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        9.15804388e-01,  1.20271764e+00,  1.24977872e+00,  6.66296639e-01,
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        1.03445095e+00,  7.51939492e-01,  1.10945565e+00,  8.22181916e-01,
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        1.25278498e+00,  1.39813137e+00,  1.20582934e+00,  6.57903432e-01,
        1.27553254e+00,  7.21452640e-01,  1.02371526e+00,  1.26120510e+00,
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        4.49113050e-01,  6.61793181e-01,  4.36235537e-01,  1.37059607e+00,
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        8.92945075e-01,  1.23662311e+00,  1.44604239e+00,  1.04028395e+00,
        1.41384259e+00,  1.30702356e+00,  6.23929753e-01,  1.45019034e+00,
        1.55587808e-01,  1.03967372e+00,  1.42042122e+00,  9.89534510e-01,
        9.30416395e-01,  1.24894837e+00,  3.71170442e-01,  1.24485322e+00,
        1.33057505e+00,  1.32888868e+00, -6.11096484e-01,  9.79612084e-01,
        9.45659846e-01,  1.32043934e+00,  1.25911360e+00,  9.05745779e-01,
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        1.00947881e+00,  1.54363117e-01,  1.56628418e+00,  1.08248447e+00,
        1.07404517e+00,  8.25827764e-01,  1.36673433e+00, -2.54165937e-01,
       -5.75497666e-02,  1.26337681e+00, -4.83037031e-01,  1.11729807e+00,
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        1.50774236e+00,  1.25078822e+00,  9.76436086e-01,  1.48403438e+00,
        9.99546130e-01,  1.21180690e+00,  1.31359884e+00,  1.36624856e+00,
        1.29507346e+00,  1.12058908e+00,  8.84672655e-01,  1.31638910e+00,
       -8.14078110e-01,  1.17085891e+00,  8.06064686e-01,  8.84126373e-01,
        1.05415375e+00,  7.75707744e-01, -1.20001901e+00,  1.36376937e+00,
        1.07215341e+00,  1.03435056e+00,  9.15114263e-01,  1.44667498e+00,
        9.90932600e-01, -3.03496641e-02,  1.40006420e-01,  7.83776897e-01,
        8.03288415e-01,  8.16869924e-01, -2.85310198e-01,  1.07425496e+00,
       -3.36408873e-02,  1.23767583e+00,  1.31659807e+00,  1.18628956e+00,
       -2.43079882e-01,  1.20917928e+00,  1.17743461e+00,  1.22320510e+00,
        6.43809489e-01,  1.12827623e+00,  1.50181028e+00,  1.05820595e+00,
        1.45156630e-01,  8.43267633e-01,  1.17421276e+00,  1.98508863e-01,
        6.57252198e-01, -1.67704845e-01,  3.64151440e-01,  9.19726540e-01,
        1.22044852e+00,  1.18440603e+00,  1.39661402e+00,  1.34731825e+00,
        7.84989681e-01,  1.25807466e+00,  1.52222696e+00,  1.57072059e+00,
       -7.29482961e-01,  1.31175844e+00,  1.23842037e+00,  1.08085992e+00,
        1.19029693e+00,  1.43358931e+00,  1.24744117e+00,  1.24303085e+00,
        5.69910769e-01,  8.58140831e-01,  9.83828123e-01,  1.02673792e+00,
        8.64850073e-01,  6.84406319e-01,  1.36368348e+00,  1.22433675e+00,
        1.51307278e+00,  7.20740381e-01,  1.27647822e+00,  9.05081706e-01,
       -5.83157089e-01,  1.22242264e+00,  1.08572199e+00,  4.25637070e-01,
        1.37000945e+00,  7.10221373e-01,  1.07990597e+00,  7.00187194e-01,
        1.10698001e+00,  1.40483172e+00,  9.85194212e-01,  1.56432936e+00,
        1.23526500e+00, -2.67699044e-01,  1.14385658e+00,  1.27840015e+00,
        1.00488481e+00,  1.08128106e+00,  1.27843616e+00,  9.20643102e-01,
        1.09666017e+00,  1.03854140e+00,  1.37840608e+00,  1.54853699e+00,
        1.38178897e+00,  1.03170348e+00,  1.27730853e+00,  1.12552149e+00,
        1.35993719e+00,  1.02082552e+00,  1.42658654e+00,  1.19045013e+00,
        9.63838665e-01,  1.05768381e+00,  1.09364551e+00,  1.42359540e+00,
        1.21088913e+00,  1.43483086e+00,  1.17339310e+00,  1.40621421e+00,
        1.36135333e+00,  1.13342996e+00,  1.21157564e+00,  1.26143700e+00,
        6.88547352e-01,  1.19236963e+00,  1.15784764e+00,  9.64586292e-01,
        1.09538813e+00,  1.55700092e+00,  1.20614864e+00,  1.33072868e+00,
        7.15211737e-01,  1.24102689e+00,  1.42966627e+00,  9.61176671e-01,
        9.51414369e-01,  1.01570673e+00,  9.87145652e-01,  1.29921178e+00,
        1.39781798e+00,  1.12072320e+00,  9.84423815e-01,  1.09640851e+00,
        1.11880048e+00,  6.71295773e-01,  1.24751896e+00,  4.93166755e-01,
        1.31644471e+00,  1.16189870e+00,  1.39524765e+00,  1.38968747e+00,
        8.49982032e-01,  1.04061345e+00,  1.39999538e+00,  1.35133547e+00,
        1.00885025e+00,  1.26080649e+00,  6.61191402e-01,  1.18617010e+00,
        1.44605869e+00,  6.07330377e-01,  5.75998723e-01,  1.23673268e+00,
        1.40796845e+00,  3.66000418e-01,  8.91250954e-01,  1.25486598e+00,
        1.13756857e+00,  7.79078387e-01,  1.45912266e+00,  9.28153899e-01,
        1.12289082e+00,  1.20735028e+00,  7.57854895e-01,  3.23471407e-02,
        1.47875667e+00,  1.29719360e+00,  9.32486742e-01,  1.14830635e+00,
        1.49124384e+00,  3.88442543e-01,  9.40699299e-01,  8.72395357e-01,
        1.16889057e+00,  9.48967164e-01,  1.34141191e+00,  1.01673775e+00,
        1.29628057e+00,  1.08619316e+00,  1.21902933e+00,  1.29238979e+00,
        1.29022693e+00,  7.70982725e-01,  1.19573861e+00,  1.23658217e+00,
        1.32970270e+00,  1.07422807e+00, -9.16204267e-01,  1.24155235e+00,
        1.94345961e+00,  4.97968359e-01,  1.25467085e+00,  1.45935073e+00,
        9.00711268e-01,  1.38717515e+00,  1.29032945e+00, -9.47868375e-02,
        1.10692036e+00,  1.00644238e+00,  4.35397125e-01,  1.30202439e+00,
        1.02421299e+00,  1.30695648e+00,  5.13124978e-01,  1.24719307e+00,
        1.36896958e+00,  1.52214481e+00,  9.33154732e-01,  7.34959501e-01,
        1.04309826e+00,  1.18507701e+00,  1.05391714e+00,  5.25348575e-01,
        2.82973819e-01,  7.79053332e-01,  8.37287908e-01,  3.61189790e-01,
       -1.19513153e+00,  4.00125413e-02,  8.72433268e-01, -5.38525457e-02])

25.3. Plot the Actual Distribution against the Normal Approximation (based on Strong Instrument Assumption)#

val = np.arange(-5,5.5,0.05)
var = (1/beta**2)*(1/100)   # theoretical variance of IV
sd = np.sqrt(var)

normal_dist = np.random.normal(0,sd,val.shape[0])

# plotting both distibutions on the same figure
fig = sns.kdeplot(IVEst-1, shade=True, color="r")
fig = sns.kdeplot(normal_dist, shade=True, color="b")

plt.title("Actual Distribution vs Gaussian")
plt.xlabel('IV Estimator -True Effect')
plt.xlim(-5,5)
(-5.0, 5.0)
../_images/79267020d100199abd190ac78e7866daeacaf042c6076325b7380bd84ed3ae9a.png
rejection_frequency = np.sum(( np.abs(IVEst-1)/sd > 1.96))/B
print("Rejection Frequency is {} ,while we expect it to be .05".format(rejection_frequency))
Rejection Frequency is 0.079 ,while we expect it to be .05