def get_comparison(regressions, export: str = ""):
"""Compare regression results between Panel Data regressions.
Parameters
----------
regressions : Dict
Dictionary with regression results.
export : str
Format to export data
Returns
-------
Returns a PanelModelComparison which shows an overview of the different regression results.
"""
comparison = {}
for regression_type, data in regressions.items():
if regression_type == "OLS":
continue
if data["model"]:
comparison[regression_type] = data["model"]
if not comparison:
# When the dictionary is empty, it means no Panel regression
# estimates are available and thus the function will have no output
return console.print(
"No Panel regression estimates available. Please use the "
"command 'panel' before using this command.")
comparison_result = compare(comparison)
console.print(comparison_result)
if export:
results_as_html = comparison_result.summary.tables[0].as_html()
df = pd.read_html(results_as_html, header=0, index_col=0)[0]
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"regressions_compare",
df,
)
return comparison_result
def balancing_tests_cohort_results(df, exog):
post_exposure1 = PanelOLS(df.adult,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton1 = post_exposure1.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
post_exposure2 = PanelOLS(df.below_median_age_restr,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton2 = post_exposure2.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
post_exposure3 = PanelOLS(df.sex_ratio,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton3 = post_exposure3.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
post_exposure4 = PanelOLS(df.have_adults_patch,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton4 = post_exposure4.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
return (compare(
{
'Size of cohort': result_balancing_canton1,
'Below median age': result_balancing_canton2,
'Sex ratio': result_balancing_canton3,
'Have families': result_balancing_canton4
},
stars=True))
def balancing_tests_cantonal_results(df, exog):
##These are the conditional results
##between countries as= asylum seekers
mod_balancing = PanelOLS(df.share_AS_between * 100,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton = mod_balancing.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
mod_balancing2 = PanelOLS(df.share_AS_within * 100,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton2 = mod_balancing2.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
mod_balancing3 = PanelOLS(df.sex_ratio_AS_ntc * 100,
exog,
entity_effects=True,
time_effects=True,
singletons=False)
result_balancing_canton3 = mod_balancing3.fit(cov_type='clustered',
clusters=df.id_e,
singletons=False)
return (compare(
{
'Between countries': result_balancing_canton,
'Within countries': result_balancing_canton2,
'Sex ratio': result_balancing_canton3
},
stars=True))
# fix assetclasslevel3, cluster time + ticker
mod = PanelOLS.from_formula(
'cd ~ 1 + cdlag1 + volume + pd + age + assetclasslevel3', data=test0)
fit03 = mod.fit(cov_type='clustered', cluster_time=True, cluster_entity=True)
# fix year, cluster time + ticker
mod = PanelOLS.from_formula('cd ~ 1 + cdlag1 + volume + pd + TimeEffects',
data=test0)
fit04 = mod.fit(cov_type='clustered', cluster_time=True, cluster_entity=True)
# Compare
print(
compare({
'fixclass1': fit01,
'fixclass2': fit02,
'fixclass3': fit03,
'fixyear': fit04
}))
# In[13]:
# fix time, cluster time + ticker
test1 = test.set_index(['ticker', 'year']) #entity and time multi-index
mod = PanelOLS.from_formula('cd ~ 1 + cdlag1 + volume + pd + TimeEffects',
data=test1)
fit1 = mod.fit(cov_type='clustered', cluster_time=True, cluster_entity=True)
# fix time + ticker, cluster time + ticker
test2 = test.set_index(['ticker', 'year'])
mod = PanelOLS.from_formula(
'cd ~ 1 + cdlag1 + volume + pd + EntityEffects + TimeEffects', data=test2)
# print(data1)
d = pd.Categorical(data1['Date'])
data1 = data1.set_index(['ID', 'Date'])
data1['Date'] = d
# print(data1)
exog_vars = [
'Kilo', 'Brakes', 'Range', 'Speed', 'RPM', 'Engine fuel rate', 'Date'
]
a = ['Kilo', 'Brakes', 'Range', 'Speed', 'RPM', 'Engine fuel rate']
print(data1[a])
exog = sm.add_constant(data1[exog_vars])
exog1 = sm.add_constant(data1[a])
mod = PanelOLS(data1['Accelerator pedal position'],
exog,
entity_effects=True,
time_effects=False)
mod1 = PooledOLS(data1['Accelerator pedal position'], exog1)
mod2 = RandomEffects(data1['Accelerator pedal position'], exog1)
mod3 = BetweenOLS(data1['Accelerator pedal position'], exog1)
res = mod.fit()
pooled_res = mod1.fit()
re_res = mod2.fit()
be_res = mod3.fit()
print(res)
print(compare({'Pooled': pooled_res, 'RE': re_res, 'BE': be_res}))
if __name__ == '__main__':
pass
def baseline_results_women(df):
CPRT_baseline_female = df.groupby(by=['sex'])
CPRT_baseline_women = CPRT_baseline_female.get_group("F")
CPRT_baseline_womenage = CPRT_baseline_women[~(
CPRT_baseline_women['age'] <= 18)]
mi_data_women = CPRT_baseline_womenage.set_index(["id_e_t", "id_a"])
exog_vars = [
"kid012_all", "all_exp_13", "all_exp_14", "all_exp_15", "all_exp_16",
"all_exp_17", "all_exp_18", "all_exp_19", "all_exp_20", "all_exp_21",
"all_exp_22", "all_exp_23", "all_exp_24", "all_exp_25", "all_exp_26",
"all_exp_27", "all_exp_28", "all_exp_29", "all_exp_30", "all_exp_31",
"all_exp_32", "all_exp_33", "all_exp_34", "all_exp_35", "all_exp_36",
"all_exp_37", "all_exp_38", "all_exp_39", "all_exp_40", "all_exp_41",
"all_exp_42", "all_exp_43", "all_exp_44", "all_exp_45", "all_exp_46",
"all_exp_47", "all_exp_48", "all_exp_49", "all_exp_50", "all_exp_51",
"all_exp_52", "all_exp_53", "all_exp_54", "all_exp_55", "all_exp_56",
"all_exp_57", "all_exp_58", "all_exp_59", "all_exp_60", "all_exp_61",
"all_exp_62", "all_exp_63", "all_exp_64", "all_exp_65", "all_exp_66",
"all_exp_67", "all_exp_68", "all_exp_69", "all_exp_70"
]
exog_women = sm.add_constant(mi_data_women[exog_vars])
CPRT_baseline_womenage.head()
mod_women = PanelOLS(mi_data_women.crime_rate_all_violent_p30,
exog_women,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=False)
res_women = mod_women.fit(cov_type='clustered',
cluster=mi_data_women.id_e,
singletons=False)
CPRT_baseline_womenage_sub = CPRT_baseline_womenage[(
CPRT_baseline_womenage['allmk_periode'] == 1)]
mi_data2_women = CPRT_baseline_womenage_sub.set_index(["id_a", "id_e_t"])
exog_vars2 = [
"kid012_all", "all_exp_13", "all_exp_14", "all_exp_15", "all_exp_16",
"all_exp_17", "all_exp_18", "all_exp_19", "all_exp_20", "all_exp_21",
"all_exp_22", "all_exp_23", "all_exp_24", "all_exp_25", "all_exp_26",
"all_exp_27", "all_exp_28", "all_exp_29", "all_exp_30", "all_exp_31",
"all_exp_32", "all_exp_33", "all_exp_34", "all_exp_35", "all_exp_36",
"all_exp_37", "all_exp_38", "all_exp_39", "all_exp_40", "all_exp_41",
"all_exp_42", "all_exp_43", "all_exp_44", "all_exp_45", "all_exp_46",
"all_exp_47", "all_exp_48", "all_exp_49", "all_exp_50", "all_exp_51",
"all_exp_52", "all_exp_53", "all_exp_54", "all_exp_55", "all_exp_56",
"all_exp_57", "all_exp_58", "all_exp_59", "all_exp_60", "all_exp_61",
"all_exp_62", "all_exp_63", "all_exp_64", "all_exp_65", "all_exp_66",
"all_exp_67", "all_exp_68", "all_exp_69", "all_exp_70", "all_exp_71"
]
exog2_women = sm.add_constant(mi_data2_women[exog_vars2])
mod2_women = PanelOLS(mi_data2_women.crime_rate_all_violent_p30,
exog2_women,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=False)
res2_women = mod2_women.fit(cov_type='clustered',
cluster=mi_data2_women["id_e"],
singletons=False)
CPRT_baseline_womenage_sub_sub = CPRT_baseline_womenage[(
CPRT_baseline_womenage['all_periode'] == 1)]
mi_data3_women = CPRT_baseline_womenage_sub_sub.set_index(
["id_a", "id_e_t"])
exog_vars3 = [
"kid012", "exp_all_13", "exp_all_14", "exp_all_15", "exp_all_16",
"exp_all_17", "exp_all_18", "exp_all_19", "exp_all_20", "exp_all_21",
"exp_all_22", "exp_all_23", "exp_all_24", "exp_all_25", "exp_all_26",
"exp_all_27", "exp_all_28", "exp_all_29", "exp_all_30", "exp_all_31",
"exp_all_32", "exp_all_33", "exp_all_34", "exp_all_35", "exp_all_36",
"exp_all_37", "exp_all_38", "exp_all_39", "exp_all_40", "exp_all_41",
"exp_all_42", "exp_all_43", "exp_all_44", "exp_all_45", "exp_all_46",
"exp_all_47", "exp_all_48", "exp_all_49", "exp_all_50", "exp_all_51",
"exp_all_52", "exp_all_53", "exp_all_54", "exp_all_55", "exp_all_56",
"exp_all_57", "exp_all_58", "exp_all_59", "exp_all_60", "exp_all_61",
"exp_all_62", "exp_all_63", "exp_all_64", "exp_all_65", "exp_all_66",
"exp_all_67", "exp_all_68", "exp_all_69", "exp_all_70", "exp_all_71"
]
exog3_women = sm.add_constant(mi_data3_women[exog_vars3])
mod3_women = PanelOLS(mi_data3_women.crime_rate_all_violent_p30,
exog3_women,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=False)
res3_women = mod3_women.fit(cov_type='clustered',
cluster=mi_data3_women["id_e"],
singletons=False)
##Table 5 column 4 women
CPRT_baseline_womenage_sub4 = CPRT_baseline_womenage[(
CPRT_baseline_womenage['mk_periode'] == 1)]
mi_data4_women = CPRT_baseline_womenage_sub4.set_index(["id_a", "id_e_t"])
##had to delete nr. 71-86
exog_vars4 = [
"MK_kid012", "exp_mk_13", "exp_mk_14", "exp_mk_15", "exp_mk_16",
"exp_mk_17", "exp_mk_18", "exp_mk_19", "exp_mk_20", "exp_mk_21",
"exp_mk_22", "exp_mk_23", "exp_mk_24", "exp_mk_25", "exp_mk_26",
"exp_mk_27", "exp_mk_28", "exp_mk_29", "exp_mk_30", "exp_mk_31",
"exp_mk_32", "exp_mk_33", "exp_mk_34", "exp_mk_35", "exp_mk_36",
"exp_mk_37", "exp_mk_38", "exp_mk_39", "exp_mk_40", "exp_mk_41",
"exp_mk_42", "exp_mk_43", "exp_mk_44", "exp_mk_45", "exp_mk_46",
"exp_mk_47", "exp_mk_48", "exp_mk_49", "exp_mk_50", "exp_mk_51",
"exp_mk_52", "exp_mk_53", "exp_mk_54", "exp_mk_55", "exp_mk_56",
"exp_mk_57", "exp_mk_58", "exp_mk_59", "exp_mk_60", "exp_mk_61",
"exp_mk_62", "exp_mk_63", "exp_mk_64", "exp_mk_65", "exp_mk_66",
"exp_mk_67", "exp_mk_68", "exp_mk_69", "exp_mk_70"
]
exog4_women = sm.add_constant(mi_data4_women[exog_vars4])
mod4_women = PanelOLS(mi_data4_women.crime_rate_all_violent_p30,
exog4_women,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=False)
res4_women = mod4_women.fit(cov_type='clustered',
cluster=CPRT_baseline_womenage_sub["id_e"],
singletons=False)
return (compare(
{
'Full': res_women,
'CC and MK': res2_women,
'CC': res3_women,
'MK': res4_women
},
stars=True))
def crime_by_type(df):
mi_data = df.set_index(["id_e_t", "id_a"])
exog_vars = [
"kid012_all", "all_exp_13", "all_exp_14", "all_exp_15", "all_exp_16",
"all_exp_17", "all_exp_18", "all_exp_19", "all_exp_20", "all_exp_21",
"all_exp_22", "all_exp_23", "all_exp_24", "all_exp_25", "all_exp_26",
"all_exp_27", "all_exp_28", "all_exp_29", "all_exp_30", "all_exp_31",
"all_exp_32", "all_exp_33", "all_exp_34", "all_exp_35", "all_exp_36",
"all_exp_37", "all_exp_38", "all_exp_39", "all_exp_40", "all_exp_41",
"all_exp_42", "all_exp_43", "all_exp_44", "all_exp_45", "all_exp_46",
"all_exp_47", "all_exp_48", "all_exp_49", "all_exp_50", "all_exp_51",
"all_exp_52", "all_exp_53", "all_exp_54", "all_exp_55", "all_exp_56",
"all_exp_57", "all_exp_58", "all_exp_59", "all_exp_60", "all_exp_61",
"all_exp_62", "all_exp_63", "all_exp_64", "all_exp_65", "all_exp_66",
"all_exp_67", "all_exp_68", "all_exp_69", "all_exp_70", "all_exp_71",
"all_exp_72", "all_exp_73", "all_exp_74", "all_exp_75", "all_exp_76",
"all_exp_77", "all_exp_78", "all_exp_79", "all_exp_80", "all_exp_81",
"all_exp_82", "all_exp_83", "all_exp_84", "all_exp_85", "all_exp_86"
]
exog_baseline_type = sm.add_constant(mi_data[exog_vars])
result_6_1 = PanelOLS(mi_data.crime_rate_violent_p30,
exog_baseline_type,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=True)
res_6_violent = result_6_1.fit(cov_type='clustered',
cluster=mi_data["id_e"])
result_6_2 = PanelOLS(mi_data.crime_rate_freedom_p30,
exog_baseline_type,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=True)
res_6_freedom = result_6_2.fit(cov_type='clustered',
cluster=mi_data["id_e"])
result_6_3 = PanelOLS(mi_data.crime_rate_sexual_p30,
exog_baseline_type,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=True)
res_6_sexual = result_6_3.fit(cov_type='clustered',
cluster=mi_data["id_e"])
result_6_4 = PanelOLS(mi_data.crime_rate_property_p30,
exog_baseline_type,
entity_effects=True,
time_effects=True,
drop_absorbed=True,
singletons=True)
res_6_property = result_6_4.fit(cov_type='clustered',
cluster=mi_data["id_e"])
return (compare(
{
'violent': res_6_violent,
'freedom': res_6_freedom,
'sexual': res_6_sexual,
'property': res_6_property
},
stars=True))
def baseline_results(df):
##first column of baseline
mi_data = df.set_index(["id_e_t", "id_a"])
exog_vars = [
"kid012_all", "all_exp_13", "all_exp_14", "all_exp_15", "all_exp_16",
"all_exp_17", "all_exp_18", "all_exp_19", "all_exp_20", "all_exp_21",
"all_exp_22", "all_exp_23", "all_exp_24", "all_exp_25", "all_exp_26",
"all_exp_27", "all_exp_28", "all_exp_29", "all_exp_30", "all_exp_31",
"all_exp_32", "all_exp_33", "all_exp_34", "all_exp_35", "all_exp_36",
"all_exp_37", "all_exp_38", "all_exp_39", "all_exp_40", "all_exp_41",
"all_exp_42", "all_exp_43", "all_exp_44", "all_exp_45", "all_exp_46",
"all_exp_47", "all_exp_48", "all_exp_49", "all_exp_50", "all_exp_51",
"all_exp_52", "all_exp_53", "all_exp_54", "all_exp_55", "all_exp_56",
"all_exp_57", "all_exp_58", "all_exp_59", "all_exp_60", "all_exp_61",
"all_exp_62", "all_exp_63", "all_exp_64", "all_exp_65", "all_exp_66",
"all_exp_67", "all_exp_68", "all_exp_69", "all_exp_70", "all_exp_71",
"all_exp_72", "all_exp_73", "all_exp_74", "all_exp_75", "all_exp_76",
"all_exp_77", "all_exp_78", "all_exp_79", "all_exp_80", "all_exp_81",
"all_exp_82", "all_exp_83", "all_exp_84", "all_exp_85", "all_exp_86"
]
exog_baseline = sm.add_constant(mi_data[exog_vars])
mod = PanelOLS(mi_data.crime_rate_all_violent_p30,
exog_baseline,
entity_effects=True,
time_effects=True,
singletons=False)
res = mod.fit(cov_type='clustered',
clusters=mi_data.id_e,
singletons=False)
##second column of baseline results
CPRT_baseline_maleage_sub = df[(df['allmk_periode'] == 1)]
mi_data2 = CPRT_baseline_maleage_sub.set_index(["id_a", "id_e_t"])
exog_vars2 = [
"kid012_all", "all_exp_13", "all_exp_14", "all_exp_15", "all_exp_16",
"all_exp_17", "all_exp_18", "all_exp_19", "all_exp_20", "all_exp_21",
"all_exp_22", "all_exp_23", "all_exp_24", "all_exp_25", "all_exp_26",
"all_exp_27", "all_exp_28", "all_exp_29", "all_exp_30", "all_exp_31",
"all_exp_32", "all_exp_33", "all_exp_34", "all_exp_35", "all_exp_36",
"all_exp_37", "all_exp_38", "all_exp_39", "all_exp_40", "all_exp_41",
"all_exp_42", "all_exp_43", "all_exp_44", "all_exp_45", "all_exp_46",
"all_exp_47", "all_exp_48", "all_exp_49", "all_exp_50", "all_exp_51",
"all_exp_52", "all_exp_53", "all_exp_54", "all_exp_55", "all_exp_56",
"all_exp_57", "all_exp_58", "all_exp_59", "all_exp_60", "all_exp_61",
"all_exp_62", "all_exp_63", "all_exp_64", "all_exp_65", "all_exp_66",
"all_exp_67", "all_exp_68", "all_exp_69", "all_exp_70", "all_exp_71",
"all_exp_72", "all_exp_73", "all_exp_74", "all_exp_75", "all_exp_76",
"all_exp_77", "all_exp_78", "all_exp_79", "all_exp_80", "all_exp_81",
"all_exp_82", "all_exp_83", "all_exp_84", "all_exp_85", "all_exp_86"
]
exog2 = sm.add_constant(mi_data2[exog_vars2])
mod2 = PanelOLS(mi_data2.crime_rate_all_violent_p30,
exog2,
entity_effects=True,
time_effects=True,
singletons=False)
res2 = mod2.fit(cov_type='clustered',
clusters=mi_data2.id_e,
singletons=False)
##third column of baseline results
CPRT_baseline_maleage_sub_sub = df[(df['all_periode'] == 1)]
CPRT_baseline_maleage_sub_sub = CPRT_baseline_maleage_sub_sub.drop(
['kid012_all'], axis=1)
CPRT_baseline_maleage_sub_sub = CPRT_baseline_maleage_sub_sub.rename(
columns={"kid012": "kid012_all"})
mi_data3 = CPRT_baseline_maleage_sub_sub.set_index(["id_a", "id_e_t"])
exog_vars3 = [
"kid012_all", "exp_all_13", "exp_all_14", "exp_all_15", "exp_all_16",
"exp_all_17", "exp_all_18", "exp_all_19", "exp_all_20", "exp_all_21",
"exp_all_22", "exp_all_23", "exp_all_24", "exp_all_25", "exp_all_26",
"exp_all_27", "exp_all_28", "exp_all_29", "exp_all_30", "exp_all_31",
"exp_all_32", "exp_all_33", "exp_all_34", "exp_all_35", "exp_all_36",
"exp_all_37", "exp_all_38", "exp_all_39", "exp_all_40", "exp_all_41",
"exp_all_42", "exp_all_43", "exp_all_44", "exp_all_45", "exp_all_46",
"exp_all_47", "exp_all_48", "exp_all_49", "exp_all_50", "exp_all_51",
"exp_all_52", "exp_all_53", "exp_all_54", "exp_all_55", "exp_all_56",
"exp_all_57", "exp_all_58", "exp_all_59", "exp_all_60", "exp_all_61",
"exp_all_62", "exp_all_63", "exp_all_64", "exp_all_65", "exp_all_66",
"exp_all_67", "exp_all_68", "exp_all_69", "exp_all_70", "exp_all_71",
"exp_all_72", "exp_all_73", "exp_all_74", "exp_all_75", "exp_all_76",
"exp_all_77", "exp_all_78", "exp_all_79", "exp_all_80", "exp_all_81",
"exp_all_82", "exp_all_83", "exp_all_84", "exp_all_85", "exp_all_86"
]
exog3 = sm.add_constant(mi_data3[exog_vars3])
mod3 = PanelOLS(mi_data3.crime_rate_all_violent_p30,
exog3,
entity_effects=True,
time_effects=True,
singletons=False)
res3 = mod3.fit(cov_type='clustered',
clusters=mi_data3.id_e,
singletons=False)
##4th column
CPRT_baseline_maleage_sub4 = df[(df['mk_periode'] == 1)]
mi_data4 = CPRT_baseline_maleage_sub4.set_index(["id_a", "id_e_t"])
exog_vars4 = [
"MK_kid012", "exp_mk_13", "exp_mk_14", "exp_mk_15", "exp_mk_16",
"exp_mk_17", "exp_mk_18", "exp_mk_19", "exp_mk_20", "exp_mk_21",
"exp_mk_22", "exp_mk_23", "exp_mk_24", "exp_mk_25", "exp_mk_26",
"exp_mk_27", "exp_mk_28", "exp_mk_29", "exp_mk_30", "exp_mk_31",
"exp_mk_32", "exp_mk_33", "exp_mk_34", "exp_mk_35", "exp_mk_36",
"exp_mk_37", "exp_mk_38", "exp_mk_39", "exp_mk_40", "exp_mk_41",
"exp_mk_42", "exp_mk_43", "exp_mk_44", "exp_mk_45", "exp_mk_46",
"exp_mk_47", "exp_mk_48", "exp_mk_49", "exp_mk_50", "exp_mk_51",
"exp_mk_52", "exp_mk_53", "exp_mk_54", "exp_mk_55", "exp_mk_56",
"exp_mk_57", "exp_mk_58", "exp_mk_59", "exp_mk_60", "exp_mk_61",
"exp_mk_62", "exp_mk_63", "exp_mk_64", "exp_mk_65", "exp_mk_66",
"exp_mk_67", "exp_mk_68", "exp_mk_69", "exp_mk_70", "exp_mk_71",
"exp_mk_72", "exp_mk_73", "exp_mk_74", "exp_mk_75", "exp_mk_76",
"exp_mk_77", "exp_mk_78", "exp_mk_79", "exp_mk_80", "exp_mk_81",
"exp_mk_82", "exp_mk_83"
]
exp_mk4 = [
"exp_mk_13", "exp_mk_14", "exp_mk_15", "exp_mk_16", "exp_mk_17",
"exp_mk_18", "exp_mk_19", "exp_mk_20", "exp_mk_21", "exp_mk_22",
"exp_mk_23", "exp_mk_24", "exp_mk_25", "exp_mk_26", "exp_mk_27",
"exp_mk_28", "exp_mk_29", "exp_mk_30", "exp_mk_31", "exp_mk_32",
"exp_mk_33", "exp_mk_34", "exp_mk_35", "exp_mk_36", "exp_mk_37",
"exp_mk_38", "exp_mk_39", "exp_mk_40", "exp_mk_41", "exp_mk_42",
"exp_mk_43", "exp_mk_44", "exp_mk_45", "exp_mk_46", "exp_mk_47",
"exp_mk_48", "exp_mk_49", "exp_mk_50", "exp_mk_51", "exp_mk_52",
"exp_mk_53", "exp_mk_54", "exp_mk_55", "exp_mk_56", "exp_mk_57",
"exp_mk_58", "exp_mk_59", "exp_mk_60", "exp_mk_61", "exp_mk_62",
"exp_mk_63", "exp_mk_64", "exp_mk_65", "exp_mk_66", "exp_mk_67",
"exp_mk_68", "exp_mk_69", "exp_mk_70", "exp_mk_71", "exp_mk_72",
"exp_mk_73", "exp_mk_74", "exp_mk_75", "exp_mk_76", "exp_mk_77",
"exp_mk_78", "exp_mk_79", "exp_mk_80", "exp_mk_81", "exp_mk_82",
"exp_mk_83", "exp_mk_84", "exp_mk_85", "exp_mk_86", "exp_mk_87",
"exp_mk_88", "exp_mk_89", "exp_mk_90", "exp_mk_91", "exp_mk_92",
"exp_mk_93", "exp_mk_94", "exp_mk_95", "exp_mk_96", "exp_mk_97",
"exp_mk_98", "exp_mk_99"
]
exog4 = sm.add_constant(mi_data4[exog_vars4])
mod4 = PanelOLS(mi_data4.crime_rate_all_violent_p30,
exog4,
entity_effects=True,
time_effects=True,
singletons=False)
res4 = mod4.fit(cov_type='clustered',
clusters=mi_data4.id_e,
singletons=False)
##presentation
return (compare({
'Full': res,
'CC and MK': res2,
'CC': res3,
'MK': res4
},
stars=True))