<jupyter_start><jupyter_text>a  = .25, k= 4, something is not right...<jupyter_code>a = 0.25
num_params=4
num_sims = 100
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=250, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=500, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)<jupyter_output>\begin{tabular}{|c|c|c|c|c|c|}
\hline
Model &  Normal & Bootstrap & Bootstrap-pt & Bootstrap-bc & Shi (2015) \\ \hline \hline
No selection & 0.91 & 0.91 & 0.91 & 0.92 & 0.96   \\
Model 1 & 0.09 & 0.09 & 0.09 & 0.08 & 0.04   \\
Model 2 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00   \\
\hline
\end{tabular}
(array([0.91, 0.09, 0.  ]), array([0.91, 0.09, 0.  ]), array([0.91, 0.09, 0.  ]), array([0.92, 0.08, 0.  ]), array([0.96, 0.04, 0.  ]), 3.025271625981256, 7.105226534988547, 7.283411720918553)<jupyter_text>a  = .25, k= 9<jupyter_code>a = 0.25
num_params=9
num_sims = 100
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=250, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=500, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=100, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)<jupyter_output>\begin{tabular}{|c|c|c|c|c|c|}
\hline
Model &  Normal & Bootstrap & Bootstrap-pt & Bootstrap-bc & Shi (2015) \\ \hline \hline
No selection & 0.93 & 0.93 & 0.92 & 0.95 & 0.96   \\
Model 1 & 0.04 & 0.05 & 0.05 & 0.03 & 0.04   \\
Model 2 & 0.03 & 0.02 & 0.03 & 0.02 & 0.00   \\
\hline
\end{tabular}
(array([0.93, 0.04, 0.03]), array([0.93, 0.05, 0.02]), array([0.92, 0.05, 0.03]), array([0.95, 0.03, 0.02]), array([0.96, 0.04, 0.  ]), -4.238054932013599, 4.64108451002556, 4.256612245840278)<jupyter_text>a  = 0, k= 9<jupyter_code>a = 0.00 
num_params=9
num_sims = 100
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=250, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=500, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)<jupyter_output>\begin{tabular}{|c|c|c|c|c|c|}
\hline
Model &  Normal & Bootstrap & Bootstrap-pt & Bootstrap-bc & Shi (2015) \\ \hline \hline
No selection & 0.99 & 0.99 & 0.99 & 0.99 & 0.99   \\
Model 1 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01   \\
Model 2 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00   \\
\hline
\end{tabular}
(array([0.99, 0.01, 0.  ]), array([0.99, 0.01, 0.  ]), array([0.99, 0.01, 0.  ]), array([0.99, 0.01, 0.  ]), array([0.99, 0.01, 0.  ]), -3.483640684176712, 2.0516972974358763, 2.9040885234449245)<jupyter_text>a  = .25<jupyter_code>a = 0.25
num_params=19
num_sims = 100
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=250, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=500, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=1000, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)<jupyter_output>\begin{tabular}{|c|c|c|c|c|c|}
\hline
Model &  Normal & Bootstrap & Bootstrap-pt & Bootstrap-bc & Shi (2015) \\ \hline \hline
No selection & 0.92 & 0.93 & 0.90 & 0.91 & 0.93   \\
Model 1 & 0.06 & 0.05 & 0.06 & 0.06 & 0.06   \\
Model 2 & 0.02 & 0.02 & 0.04 & 0.03 & 0.01   \\
\hline
\end{tabular}
(array([0.92, 0.06, 0.02]), array([0.93, 0.05, 0.02]), array([0.9 , 0.06, 0.04]), array([0.91, 0.06, 0.03]), array([0.93, 0.06, 0.01]), -6.739603738840657, 12.768050787988567, 11.124473135869243)<jupyter_text>a = 0<jupyter_code>a = 0.0
num_params=19
num_sims = 100
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=250, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data(nobs=500, a=a,  num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)<jupyter_output>\begin{tabular}{|c|c|c|c|c|c|}
\hline
Model &  Normal & Bootstrap & Bootstrap-pt & Bootstrap-bc & Shi (2015) \\ \hline \hline
No selection & 1.00 & 1.00 & 1.00 & 0.99 & 1.00   \\
Model 1 & 0.00 & 0.00 & 0.00 & 0.01 & 0.00   \\
Model 2 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00   \\
\hline
\end{tabular}
(array([1., 0., 0.]), array([1., 0., 0.]), array([1., 0., 0.]), array([0.99, 0.01, 0.  ]), array([1., 0., 0.]), -8.544728096987173, 3.6246609587803564, 4.328876391626203)<jupyter_text>evidence of power<jupyter_code>a1,a2 = np.sqrt(1.09-1), 0.00
num_params= 9
num_sims = 100

def gen_data2(nobs=1000, a1=np.sqrt(1.09-1), a2=0.00 , num_params=19):
    x = np.random.normal(scale=1., size=(nobs,1+num_params))
    e = np.random.normal(loc=0.0, scale=1.0, size=nobs)
    y = 1 + a1*x[:,0] + a2/np.sqrt(num_params)*x[:,1:num_params].sum(axis=1) + e
    return y,x,nobs
a1,a2 = np.sqrt(1.09-1), 0.00
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data2(nobs=250, a1=a1, a2=a2, num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
a1,a2 = np.sqrt(1.09**.5-1), 0.00
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data2(nobs=500, a1=a1, a2=a2, num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
a1,a2 = 0.00,np.sqrt(1.09-1)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data2(nobs=250, a1=a1, a2=a2, num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)
a1,a2 =  0.00,np.sqrt(1.09**.5-1)
setup_shi_ex  = lambda yn,xn: setup_shi(yn,xn,num_params=num_params)
gen_data_ex = lambda : gen_data2(nobs=500, a1=a1, a2=a2, num_params=num_params)
mc_out = vuong_tests5.monte_carlo(num_sims,gen_data_ex,setup_shi_ex)
vuong_tests5.print_mc(mc_out)
print(mc_out)<jupyter_output>\begin{tabular}{|c|c|c|c|c|c|}
\hline
Model &  Normal & Bootstrap & Bootstrap-pt & Bootstrap-bc & Shi (2015) \\ \hline \hline
No selection & 0.54 & 0.56 & 0.51 & 0.51 & 0.58   \\
Model 1 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00   \\
Model 2 & 0.46 & 0.44 & 0.49 & 0.49 & 0.42   \\
\hline
\end{tabular}
(array([0.54, 0.  , 0.46]), array([0.56, 0.  , 0.44]), array([0.51, 0.  , 0.49]), array([0.51, 0.  , 0.49]), array([0.58, 0.  , 0.42]), -13.569542451255797, 4.400747294594443, 5.297594067113448)
{'fraction_non_alphanumeric': 0.1735557768924303, 'fraction_numerical': 0.1291085657370518, 'mean_word_length': 5.026256564141035, 'pattern_counts': {'":': 0, '<': 19, '<?xml version=': 0, '>': 19, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '231033', 'license': 'MIT', 'n_tokens_mistral': 4544, 'n_tokens_neox': 3846, 'n_words': 591, 'path': 'overlapping_reg/pre_summer2021/overlap_shi_ex_pt_slow-Copy1.ipynb', 'repo_name': 'ericschulman/nash', 'size': '8032'}