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robertopucp
GitHub Repository: robertopucp/1eco35_2022_2
 Path: blob/main/Trabajo_final/grupo8/TrabajoFinal_grupo8.ipynb
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Kernel: Python 3 (ipykernel)
# import libraries 

import pandas as pd 
import numpy as np
import re 
from tqdm import tqdm  # controlar el tiempo en un loop
import os


# linear model library

import statsmodels.api as sm  # linear regression utiliza todas las columnas de base de datos 
import statsmodels.formula.api as smf  # linear regression usa uan formula
from sklearn import datasets, linear_model # models 
from sklearn.metrics import mean_squared_error, r2_score
from linearmodels.iv import IV2SLS # for IV regression
import warnings
warnings.filterwarnings('ignore') # eliminar warning messages 


# Export latex table 

from pystout import pystout

user = os.getlogin()   # Username
os.chdir(f"C:/Users/{user}/Documents/GitHub/1ECO35_2022_2/Trabajo_final/grupo8") # Set directorio


# laod dataset

data = pd.read_stata(r"../datos/mss_repdata.dta",
                           convert_categoricals=False)

# convert_categoricals=False: No se lee las etiquetas de valor 

data

# Extraer year
# con .month se puede extraer el mes 
# con .day se puede extraer el día 

data['time_year'] = pd.DatetimeIndex(data['year']).year - 1978
data['time_year']
0 3 1 4 2 5 3 6 4 7 .. 738 17 739 18 740 19 741 20 742 21 Name: time_year, Length: 743, dtype: int64
dummys = pd.get_dummies(data["ccode"].astype(int), prefix = "ccode", dummy_na=False)
dummys.columns
# se convierte a entero repdata["ccode"].astype(int)
# ccode: código por país 
# dummy_na=False 
# capturar varibbles omitidas invariantes de cada país
Index(['ccode_404', 'ccode_420', 'ccode_432', 'ccode_433', 'ccode_434', 'ccode_435', 'ccode_436', 'ccode_437', 'ccode_438', 'ccode_439', 'ccode_450', 'ccode_451', 'ccode_452', 'ccode_461', 'ccode_471', 'ccode_475', 'ccode_481', 'ccode_482', 'ccode_483', 'ccode_484', 'ccode_490', 'ccode_500', 'ccode_501', 'ccode_510', 'ccode_516', 'ccode_517', 'ccode_520', 'ccode_522', 'ccode_530', 'ccode_540', 'ccode_541', 'ccode_551', 'ccode_552', 'ccode_553', 'ccode_560', 'ccode_565', 'ccode_570', 'ccode_571', 'ccode_572', 'ccode_580', 'ccode_625'], dtype='object')
data = pd.concat([ data , dummys], axis = 1 )
# concantenar ambas bases de datos de manera horizontal (axis = 1)

# Creación del trend_country effects : multiplicación de las dummy por país y la variable temporal 
# capturar variables omitidas variantes en el tiempo por cada país. 

i = 0

while i < 41:  # 41 por el tema de indexing pues en python la posición inicial es cero. 
    var = dummys.columns[i]+"_"+"time"  # creamos el nombre de cada variable
    data[var]  = data[dummys.columns[i]]*data["time_year"] 
    
    # multiplicacón de variables: dummy país * variable temporal

    i = i + 1

# observamos para país y la variable temporarl. 
data[['ccode','time_year']].iloc[0:40,:]

# Seleccionamos las variables para las estadísticas descriptivas

table1 = data.loc[:,["NDVI_g", "tot_100","trade_pGDP",
                                 "pop_den_rur", "land_crop", "va_agr", "va_ind_manf"
]]

table1 

# selccionamos los estadísticos de interés: media, error estándar y cantidad de observaciones

summary_table = table1.describe().loc[["mean","std","count"]]
summary_table

summary_table = table1.describe().loc[["mean","std","count"]].T
summary_table
# .t permite tranponer el DataFrame

table1.columns
Index(['NDVI_g', 'tot_100', 'trade_pGDP', 'pop_den_rur', 'land_crop', 'va_agr', 'va_ind_manf'], dtype='object')
table1.columns

new_names = ["Tasa de variación del índice de vegetación",
               "Términos de intercambio",
               "Porcentaje de las exportaciones respecto al PBI",
               "Densidad poblacional rural",
               "Porcentaje de tierra cultivable en uso",
               "Valor agregado del sector agricultura respecto PBI",
               "Valor agregado del sector manufacturero respecto PBI"
]

# unión de listas bajo la estructura diccionario

dict( zip( table1.columns, new_names) )
{'NDVI_g': 'Tasa de variación del índice de vegetación', 'tot_100': 'Términos de intercambio', 'trade_pGDP': 'Porcentaje de las exportaciones respecto al PBI', 'pop_den_rur': 'Densidad poblacional rural', 'land_crop': 'Porcentaje de tierra cultivable en uso', 'va_agr': 'Valor agregado del sector agricultura respecto PBI', 'va_ind_manf': 'Valor agregado del sector manufacturero respecto PBI'}
# Customize summary table 

index_nuevos_nombres = dict( zip( table1.columns, new_names) )

columns_nuevos_nombres = {
    "mean": "Mean",
    "std": "Standard Deviation",
    "count": "Observations",
}

# Rename rows (indexes) and columns
summary_table.rename(index=index_nuevos_nombres, columns=columns_nuevos_nombres, inplace=True)


summary_table

# Export the DataFrame to LaTeX
# \ permite esccribir código extenso en lineas diferentes

summary_table.style.format(subset="Mean", precision=2).format(subset="Standard Deviation", precision=2).format(subset="Observations", precision=0).to_latex(
    "summary2.tex",
caption="Descriptive Statistics",
    column_format = "lccc"   # l: left, c:center , 
) 

#Columns format
summary_table.style.format(subset="Mean", precision=2).format(subset="Standard Deviation", precision=2).format(subset="Observations", precision=0)

Regresiones modelo 1

y = data['any_prio']

# add constant

X = sm.add_constant(data.loc[:,["GPCP_g", "GPCP_g_l"]])

# sm function

ols_model1 = sm.OLS(y, X).fit()

# fit() permite correr la regresión

print(ols_model1.summary())

# Robust standar error

ols_model1_rb = sm.OLS(y, X).fit(cov_type = "HC1")
print(ols_model1_rb.summary())
OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 1.008 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.366 Time: 20:42:58 Log-Likelihood: -448.04 No. Observations: 743 AIC: 902.1 Df Residuals: 740 BIC: 915.9 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 0.2697 0.016 16.449 0.000 0.238 0.302 GPCP_g -0.0288 0.085 -0.339 0.735 -0.196 0.138 GPCP_g_l -0.1204 0.086 -1.397 0.163 -0.290 0.049 ============================================================================== Omnibus: 189.379 Durbin-Watson: 0.530 Prob(Omnibus): 0.000 Jarque-Bera (JB): 159.939 Skew: 1.044 Prob(JB): 1.86e-35 Kurtosis: 2.104 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 1.014 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.363 Time: 20:42:59 Log-Likelihood: -448.04 No. Observations: 743 AIC: 902.1 Df Residuals: 740 BIC: 915.9 Df Model: 2 Covariance Type: HC1 ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ const 0.2697 0.016 16.374 0.000 0.237 0.302 GPCP_g -0.0288 0.090 -0.321 0.748 -0.205 0.147 GPCP_g_l -0.1204 0.087 -1.391 0.164 -0.290 0.049 ============================================================================== Omnibus: 189.379 Durbin-Watson: 0.530 Prob(Omnibus): 0.000 Jarque-Bera (JB): 159.939 Skew: 1.044 Prob(JB): 1.86e-35 Kurtosis: 2.104 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors are heteroscedasticity robust (HC1) 
#alternative robust standar error
ols_model1_rb1 = sm.OLS(y, X).fit(cov_type = "HC1") # Huber-White robust se

print(ols_model1_rb1.summary())
OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 1.014 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.363 Time: 20:43:07 Log-Likelihood: -448.04 No. Observations: 743 AIC: 902.1 Df Residuals: 740 BIC: 915.9 Df Model: 2 Covariance Type: HC1 ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ const 0.2697 0.016 16.374 0.000 0.237 0.302 GPCP_g -0.0288 0.090 -0.321 0.748 -0.205 0.147 GPCP_g_l -0.1204 0.087 -1.391 0.164 -0.290 0.049 ============================================================================== Omnibus: 189.379 Durbin-Watson: 0.530 Prob(Omnibus): 0.000 Jarque-Bera (JB): 159.939 Skew: 1.044 Prob(JB): 1.86e-35 Kurtosis: 2.104 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors are heteroscedasticity robust (HC1) 
# Acceder a la información de la tabla

ols_model1_rb.summary2()

ols_model1_rb.summary2().tables[1]

dir(sm.OLS(y, X))

# Lista de atributos y métodos
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_check_kwargs', '_data_attr', '_df_model', '_df_resid', '_fit_collinear', '_fit_ridge', '_fit_zeros', '_formula_max_endog', '_get_init_kwds', '_handle_data', '_init_keys', '_kwargs_allowed', '_setup_score_hess', '_sqrt_lasso', 'data', 'df_model', 'df_resid', 'endog', 'endog_names', 'exog', 'exog_names', 'fit', 'fit_regularized', 'from_formula', 'get_distribution', 'hessian', 'hessian_factor', 'information', 'initialize', 'k_constant', 'loglike', 'nobs', 'predict', 'rank', 'score', 'weights', 'wendog', 'wexog', 'whiten']
# Y estimados a partir del método predict 

sm.OLS(y, X).fit().predict()
array([0.28496978, 0.2748217 , 0.25195793, 0.27214113, 0.26302426, 0.26568972, 0.2466797 , 0.27791809, 0.25526007, 0.28224195, 0.26917588, 0.30081865, 0.28540232, 0.21866934, 0.2861791 , 0.27253843, 0.27999266, 0.27934855, 0.26917232, 0.28043463, 0.27237109, 0.26828123, 0.27929381, 0.25381756, 0.26769429, 0.27252959, 0.27744108, 0.24540483, 0.2622242 , 0.28374367, 0.22578955, 0.28753694, 0.26812988, 0.25210689, 0.2885355 , 0.26993623, 0.26967236, 0.25559903, 0.24386254, 0.2739153 , 0.30170927, 0.26866805, 0.28991521, 0.24865613, 0.26155857, 0.27105444, 0.17496034, 0.29626138, 0.29942372, 0.23988976, 0.30644488, 0.18201347, 0.27697215, 0.25379256, 0.24793872, 0.26968144, 0.29971203, 0.27894354, 0.27432929, 0.27281134, 0.28310697, 0.25005254, 0.24888163, 0.27129552, 0.27677362, 0.2524269 , 0.27155199, 0.28413026, 0.2312682 , 0.28199823, 0.27089332, 0.2498764 , 0.28802241, 0.26710212, 0.26587571, 0.26061178, 0.2816283 , 0.25395249, 0.24080911, 0.29886544, 0.26830424, 0.24838017, 0.28130712, 0.26336496, 0.25129281, 0.27310025, 0.28621181, 0.27746349, 0.28334718, 0.287771 , 0.26165149, 0.26820117, 0.26669159, 0.26500867, 0.28311481, 0.26802954, 0.27617081, 0.26463664, 0.2899249 , 0.25148575, 0.26197403, 0.2830811 , 0.27255562, 0.25724725, 0.27454112, 0.25363973, 0.27860369, 0.26622255, 0.26348373, 0.27386322, 0.2701804 , 0.26925542, 0.27306353, 0.27548579, 0.2667854 , 0.26456781, 0.26831091, 0.28676397, 0.27466626, 0.24652724, 0.27373615, 0.26641863, 0.25932942, 0.26635622, 0.26933033, 0.27183976, 0.27969916, 0.27150216, 0.26157324, 0.27072618, 0.27180415, 0.27647364, 0.25294516, 0.24895213, 0.2898935 , 0.28356814, 0.28855814, 0.28652282, 0.23272215, 0.28414438, 0.26690943, 0.21789304, 0.30029623, 0.29493282, 0.21974803, 0.26397938, 0.27688917, 0.19916378, 0.29395434, 0.27651376, 0.27886489, 0.24405499, 0.23491972, 0.26913932, 0.2614215 , 0.28823138, 0.23593892, 0.26834563, 0.28889719, 0.26150323, 0.25476589, 0.28021396, 0.2785566 , 0.28739284, 0.25638399, 0.2685143 , 0.26601195, 0.266387 , 0.28047375, 0.2795782 , 0.24956602, 0.27618855, 0.21320236, 0.28918143, 0.28556657, 0.29926377, 0.23754775, 0.27906998, 0.26678421, 0.24210984, 0.26576587, 0.31459122, 0.27405136, 0.24264627, 0.277868 , 0.29356047, 0.28646617, 0.23867612, 0.27209826, 0.25694241, 0.2604217 , 0.27243465, 0.28738483, 0.27930561, 0.25147996, 0.24456337, 0.28121169, 0.26633678, 0.23019748, 0.29172668, 0.28749903, 0.24031677, 0.27051027, 0.26277372, 0.28804177, 0.2257001 , 0.27398469, 0.27986473, 0.27040816, 0.25399174, 0.28285273, 0.28265373, 0.28293827, 0.25610697, 0.26501434, 0.27097668, 0.26839272, 0.27354068, 0.27955355, 0.24731268, 0.28533431, 0.24160057, 0.28978968, 0.29282641, 0.24800285, 0.24949416, 0.23826094, 0.26310096, 0.25438468, 0.28716589, 0.29769171, 0.26526959, 0.26059674, 0.25591369, 0.25387336, 0.29156528, 0.26901609, 0.27783372, 0.26318861, 0.28377692, 0.28088419, 0.28861658, 0.2754023 , 0.21938379, 0.26384442, 0.27896696, 0.25244708, 0.28188856, 0.25745628, 0.28301216, 0.24279603, 0.30309932, 0.26144509, 0.27874158, 0.25212564, 0.27821526, 0.27051771, 0.2818894 , 0.27417885, 0.25867843, 0.29059362, 0.29452066, 0.26426166, 0.25535766, 0.24858263, 0.26895224, 0.26055379, 0.26625866, 0.27383444, 0.26121317, 0.27357787, 0.27429979, 0.25721523, 0.29231413, 0.27848952, 0.27543343, 0.26381051, 0.27067454, 0.24849388, 0.27312393, 0.29277768, 0.25732122, 0.26590736, 0.25722482, 0.24220079, 0.25835867, 0.27736391, 0.29110722, 0.25071988, 0.27249154, 0.24667352, 0.28453534, 0.26808795, 0.26916287, 0.27444919, 0.27196229, 0.28122464, 0.2752372 , 0.2750082 , 0.28815601, 0.22531638, 0.27212014, 0.27465749, 0.26100753, 0.27865461, 0.260037 , 0.28519386, 0.26132007, 0.2827029 , 0.2640916 , 0.26392098, 0.26866438, 0.28346345, 0.27598611, 0.26640891, 0.29957058, 0.22487442, 0.24185624, 0.3151507 , 0.26392006, 0.25288431, 0.2861609 , 0.28180334, 0.24162488, 0.26748908, 0.2813641 , 0.29542743, 0.26492747, 0.27644114, 0.24360548, 0.28417196, 0.26034035, 0.17026035, 0.30999498, 0.27696997, 0.23869529, 0.29867055, 0.25692226, 0.27413664, 0.25267573, 0.25573401, 0.24826106, 0.26042945, 0.29312151, 0.29211235, 0.25152075, 0.29296429, 0.22099036, 0.28299408, 0.2356418 , 0.24959036, 0.28636198, 0.27331616, 0.28060294, 0.26493497, 0.28194248, 0.29095738, 0.24315764, 0.27541201, 0.25938504, 0.26466378, 0.26737449, 0.26595197, 0.29768923, 0.26383165, 0.25954319, 0.25987779, 0.28399343, 0.26380928, 0.28600594, 0.25770208, 0.28576176, 0.25631652, 0.254864 , 0.29963553, 0.25451147, 0.27546931, 0.2578158 , 0.26132511, 0.26856981, 0.27780048, 0.27176283, 0.27174748, 0.27706396, 0.29420754, 0.23583807, 0.28863299, 0.24272018, 0.26333879, 0.28775854, 0.29702876, 0.22687502, 0.24580235, 0.30930804, 0.25007445, 0.30066286, 0.22727061, 0.29096045, 0.25035398, 0.25630239, 0.26997338, 0.28689748, 0.26815232, 0.28173516, 0.29287411, 0.27978217, 0.26405809, 0.24444535, 0.26822766, 0.27884218, 0.22996273, 0.28585741, 0.28870403, 0.24169328, 0.27014081, 0.26660939, 0.22146699, 0.30172142, 0.27983771, 0.26684136, 0.23897902, 0.27601925, 0.26925608, 0.3101956 , 0.27663425, 0.25205277, 0.2547879 , 0.27424848, 0.28229888, 0.223476 , 0.2662009 , 0.32331318, 0.26671921, 0.2668772 , 0.2074825 , 0.22536602, 0.28593027, 0.30477405, 0.26808999, 0.24947935, 0.25745349, 0.27321761, 0.2748773 , 0.27776485, 0.23928001, 0.2635465 , 0.28749362, 0.29472081, 0.21862439, 0.2676335 , 0.30080166, 0.26966232, 0.29306065, 0.23688644, 0.28930969, 0.24584524, 0.24023323, 0.26620497, 0.28865302, 0.25440343, 0.27250326, 0.28728198, 0.07583003, 0.31090373, 0.29627702, 0.236097 , 0.21528979, 0.31659791, 0.26719918, 0.29998336, 0.27639208, 0.29233801, 0.27626678, 0.23871984, 0.29270098, 0.28971898, 0.19109602, 0.29560864, 0.29507539, 0.20492604, 0.2788876 , 0.25891261, 0.17444965, 0.31339199, 0.27884491, 0.26400569, 0.22305778, 0.27565457, 0.28403302, 0.27106399, 0.28610541, 0.25836077, 0.25912874, 0.27306704, 0.27233956, 0.24449237, 0.26971896, 0.27261432, 0.26493679, 0.27394499, 0.26721449, 0.25684216, 0.27022947, 0.27542375, 0.26995902, 0.27579783, 0.28002371, 0.25709103, 0.24234488, 0.296826 , 0.26192363, 0.25365127, 0.2864295 , 0.24891512, 0.24938813, 0.28482997, 0.27828059, 0.28726476, 0.28035497, 0.27157612, 0.25801002, 0.26678263, 0.26853841, 0.27516955, 0.27874475, 0.28386285, 0.24604537, 0.28752855, 0.29526714, 0.24616023, 0.2486039 , 0.24670796, 0.26244128, 0.25179474, 0.28499766, 0.3037 , 0.26479621, 0.26168092, 0.25250814, 0.25671517, 0.28608606, 0.27361334, 0.27955091, 0.25924962, 0.28006212, 0.2549075 , 0.28531706, 0.2908116 , 0.26898266, 0.25820194, 0.24600473, 0.27005837, 0.27632638, 0.26294027, 0.27953282, 0.26117204, 0.26488158, 0.27616653, 0.27326821, 0.28086191, 0.28593079, 0.28560771, 0.26892224, 0.28822231, 0.16313024, 0.25497695, 0.32293383, 0.21249017, 0.27262115, 0.30609214, 0.16679669, 0.27783477, 0.23734724, 0.29606521, 0.26716866, 0.2454338 , 0.30372741, 0.27167846, 0.26917541, 0.24335986, 0.28579254, 0.25898906, 0.24126848, 0.29032779, 0.29051066, 0.24820739, 0.29564777, 0.22738325, 0.27717002, 0.22919359, 0.25948569, 0.28919155, 0.28430183, 0.26479902, 0.27528442, 0.2899312 , 0.28356204, 0.28107796, 0.21581487, 0.27031131, 0.25826531, 0.24656602, 0.28470031, 0.29621292, 0.25227005, 0.26035504, 0.26259964, 0.26482224, 0.26159492, 0.25209917, 0.3018435 , 0.25325603, 0.25525354, 0.30793087, 0.21737779, 0.2329495 , 0.31341325, 0.28866529, 0.23647127, 0.27112947, 0.27523367, 0.29539826, 0.28145113, 0.28730419, 0.18898682, 0.28578298, 0.22418157, 0.25214535, 0.28336495, 0.28970836, 0.29180466, 0.27580531, 0.2309655 , 0.28955376, 0.25599223, 0.27000073, 0.28290819, 0.28032227, 0.25336004, 0.25340982, 0.29365966, 0.27637294, 0.28032937, 0.28603889, 0.26034879, 0.26022068, 0.27697228, 0.24887283, 0.28978698, 0.28490459, 0.28200161, 0.28765797, 0.26949008, 0.22787498, 0.27018998, 0.28271041, 0.25357493, 0.26073737, 0.27463583, 0.2798359 , 0.24296043, 0.30306325, 0.25136765, 0.26761196, 0.2565544 , 0.28044399, 0.26945989, 0.28381678, 0.27537043, 0.24773015, 0.24674011, 0.30349319, 0.27601743, 0.22736574, 0.28421549, 0.25956987, 0.23834513, 0.2883796 , 0.26600035, 0.27435681, 0.28586025, 0.28055657, 0.25998459, 0.27036974, 0.25652407, 0.27700439, 0.28883738, 0.26650265, 0.26988379, 0.2485796 , 0.29300512, 0.26148703, 0.25306436, 0.27810453, 0.25796663, 0.25084136, 0.26911237, 0.28340842, 0.2867332 , 0.28721706, 0.26366116, 0.27082614, 0.26701527, 0.27578898, 0.28386679, 0.28645862, 0.27412183, 0.26185508, 0.28062735, 0.2602632 , 0.26023209, 0.26803586, 0.29506981, 0.24185199, 0.2570696 , 0.29029694, 0.27443058, 0.29234101, 0.22388564, 0.30235436, 0.26654653, 0.25558241, 0.27048435, 0.28396046, 0.25304765, 0.25309798, 0.31575402, 0.26484374, 0.24554492, 0.22660271, 0.28882671, 0.29258781, 0.22467728, 0.28253382, 0.28212012, 0.29394026, 0.27481921, 0.21302369, 0.30612175, 0.24754037, 0.20656903, 0.26960102, 0.28885259])
# Recordad métodos y atributos 

print(dir(ols_model1))

# predict para ello uso la función predict 

ols_model1.predict(X)

# acceso a los parámetros

ols_model1.params

# R2 y R2  ajustado

ols_model1.rsquared
ols_model1.rsquared_adj
['HC0_se', 'HC1_se', 'HC2_se', 'HC3_se', '_HCCM', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_abat_diagonal', '_cache', '_data_attr', '_data_in_cache', '_get_robustcov_results', '_is_nested', '_use_t', '_wexog_singular_values', 'aic', 'bic', 'bse', 'centered_tss', 'compare_f_test', 'compare_lm_test', 'compare_lr_test', 'condition_number', 'conf_int', 'conf_int_el', 'cov_HC0', 'cov_HC1', 'cov_HC2', 'cov_HC3', 'cov_kwds', 'cov_params', 'cov_type', 'df_model', 'df_resid', 'diagn', 'eigenvals', 'el_test', 'ess', 'f_pvalue', 'f_test', 'fittedvalues', 'fvalue', 'get_influence', 'get_prediction', 'get_robustcov_results', 'info_criteria', 'initialize', 'k_constant', 'llf', 'load', 'model', 'mse_model', 'mse_resid', 'mse_total', 'nobs', 'normalized_cov_params', 'outlier_test', 'params', 'predict', 'pvalues', 'remove_data', 'resid', 'resid_pearson', 'rsquared', 'rsquared_adj', 'save', 'scale', 'ssr', 'summary', 'summary2', 't_test', 't_test_pairwise', 'tvalues', 'uncentered_tss', 'use_t', 'wald_test', 'wald_test_terms', 'wresid']
2.0778261906828632e-05
control_formula = "any_prio"+ " ~ "+ "GPCP_g + " + "GPCP_g_l"

ols_model1 = smf.ols(control_formula, data=data).fit()

print(ols_model1.summary())
OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.000 Method: Least Squares F-statistic: 1.008 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.366 Time: 20:43:55 Log-Likelihood: -448.04 No. Observations: 743 AIC: 902.1 Df Residuals: 740 BIC: 915.9 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 0.2697 0.016 16.449 0.000 0.238 0.302 GPCP_g -0.0288 0.085 -0.339 0.735 -0.196 0.138 GPCP_g_l -0.1204 0.086 -1.397 0.163 -0.290 0.049 ============================================================================== Omnibus: 189.379 Durbin-Watson: 0.530 Prob(Omnibus): 0.000 Jarque-Bera (JB): 159.939 Skew: 1.044 Prob(JB): 1.86e-35 Kurtosis: 2.104 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. 
ols_model1_skl = linear_model.LinearRegression().fit( X, y )

ols_model1_skl.coef_ # acceso a coeficientes 

ols_model1_skl.predict(X) # predict en formato array 
ols_model1_skl.score(X,y) # R cuadrado
dir(ols_model1_skl)
mean_squared_error( y, ols_model1.predict())**0.5 # root mean square error
0.4422283305322858
# country fixed effect

index_columns = np.where( data.columns.str.contains(
    '_time$'))[0]
# indice con nombre de variables que terminan con _time

country_trend = data.columns[index_columns] # se extrae el nombre de todas las variables que terminan con _time

formula_model1 = "any_prio ~ GPCP_g + GPCP_g_l + C(ccode)" + ' + ' + ' + '.join( country_trend )

ols_model1 = smf.ols(formula_model1, data=data).fit(cov_type='cluster', cov_kwds={'groups': data['ccode']})

print(ols_model1.summary())

rmse_ol1 = round(mean_squared_error( y, ols_model1.predict())**0.5,2)

print(rmse_ol1)
OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.708 Model: OLS Adj. R-squared: 0.672 Method: Least Squares F-statistic: 162.4 Date: Sat, 10 Dec 2022 Prob (F-statistic): 6.28e-20 Time: 20:44:20 Log-Likelihood: 8.6565 No. Observations: 743 AIC: 150.7 Df Residuals: 659 BIC: 538.0 Df Model: 83 Covariance Type: cluster ===================================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------------- Intercept -0.2458 0.004 -66.965 0.000 -0.253 -0.239 C(ccode)[T.420.0] 0.4921 0.001 468.956 0.000 0.490 0.494 C(ccode)[T.432.0] 0.2600 0.008 32.651 0.000 0.244 0.276 C(ccode)[T.433.0] -0.0986 0.002 -59.007 0.000 -0.102 -0.095 C(ccode)[T.434.0] 0.2438 0.004 57.888 0.000 0.236 0.252 C(ccode)[T.435.0] 0.2377 0.009 26.715 0.000 0.220 0.255 C(ccode)[T.436.0] 0.1243 0.013 9.406 0.000 0.098 0.150 C(ccode)[T.437.0] 0.2450 0.004 61.386 0.000 0.237 0.253 C(ccode)[T.438.0] -0.0061 0.003 -2.184 0.029 -0.012 -0.001 C(ccode)[T.439.0] 0.4443 0.004 105.272 0.000 0.436 0.453 C(ccode)[T.450.0] -0.3559 0.004 -92.197 0.000 -0.363 -0.348 C(ccode)[T.451.0] -0.2257 0.002 -120.766 0.000 -0.229 -0.222 C(ccode)[T.452.0] 0.6882 0.004 159.022 0.000 0.680 0.697 C(ccode)[T.461.0] 0.4129 0.005 88.451 0.000 0.404 0.422 C(ccode)[T.471.0] 0.4257 0.004 106.398 0.000 0.418 0.433 C(ccode)[T.475.0] 0.2433 0.005 44.966 0.000 0.233 0.254 C(ccode)[T.481.0] 0.2542 0.002 162.114 0.000 0.251 0.257 C(ccode)[T.482.0] 0.2466 0.003 72.125 0.000 0.240 0.253 C(ccode)[T.483.0] 1.3664 0.008 162.199 0.000 1.350 1.383 C(ccode)[T.484.0] -0.0916 0.002 -58.163 0.000 -0.095 -0.088 C(ccode)[T.490.0] 1.3481 0.001 1581.192 0.000 1.346 1.350 C(ccode)[T.500.0] 1.2554 0.002 743.377 0.000 1.252 1.259 C(ccode)[T.501.0] 0.4613 0.005 92.925 0.000 0.452 0.471 C(ccode)[T.510.0] 0.2446 0.004 60.659 0.000 0.237 0.252 C(ccode)[T.516.0] -0.1279 0.001 -184.968 0.000 -0.129 -0.127 C(ccode)[T.517.0] -0.1171 0.001 -198.672 0.000 -0.118 -0.116 C(ccode)[T.520.0] 1.2725 0.021 59.568 0.000 1.231 1.314 C(ccode)[T.522.0] -0.0132 0.008 -1.660 0.097 -0.029 0.002 C(ccode)[T.530.0] 1.3320 0.002 620.380 0.000 1.328 1.336 C(ccode)[T.540.0] 1.2494 0.002 611.501 0.000 1.245 1.253 C(ccode)[T.541.0] 1.7636 0.004 405.215 0.000 1.755 1.772 C(ccode)[T.551.0] 0.2440 0.005 53.114 0.000 0.235 0.253 C(ccode)[T.552.0] -0.0049 0.003 -1.585 0.113 -0.011 0.001 C(ccode)[T.553.0] 0.2460 0.005 44.830 0.000 0.235 0.257 C(ccode)[T.560.0] 1.7528 0.002 796.854 0.000 1.749 1.757 C(ccode)[T.565.0] -1.4419 0.030 -47.517 0.000 -1.501 -1.382 C(ccode)[T.570.0] 0.1329 0.002 74.355 0.000 0.129 0.136 C(ccode)[T.571.0] 0.2493 0.002 110.308 0.000 0.245 0.254 C(ccode)[T.572.0] 0.2519 0.001 329.982 0.000 0.250 0.253 C(ccode)[T.580.0] 0.2488 0.002 147.237 0.000 0.245 0.252 C(ccode)[T.625.0] 0.7557 0.003 246.088 0.000 0.750 0.762 GPCP_g -0.0238 0.043 -0.550 0.582 -0.108 0.061 GPCP_g_l -0.1219 0.052 -2.352 0.019 -0.224 -0.020 ccode_404_time 0.0295 0.000 185.142 0.000 0.029 0.030 ccode_420_time -0.0160 0.000 -143.094 0.000 -0.016 -0.016 ccode_432_time 0.0078 0.001 15.618 0.000 0.007 0.009 ccode_433_time 0.0595 5.87e-05 1014.282 0.000 0.059 0.060 ccode_434_time 0.0004 0.000 2.272 0.023 4.97e-05 0.001 ccode_435_time 0.0009 0.001 1.469 0.142 -0.000 0.002 ccode_436_time 0.0369 0.001 34.562 0.000 0.035 0.039 ccode_437_time -1.254e-05 5.51e-06 -2.276 0.023 -2.33e-05 -1.74e-06 ccode_438_time 0.0297 0.000 270.544 0.000 0.030 0.030 ccode_439_time -0.0032 0.000 -19.303 0.000 -0.004 -0.003 ccode_450_time 0.1090 0.000 656.335 0.000 0.109 0.109 ccode_451_time 0.0786 0.000 259.266 0.000 0.078 0.079 ccode_452_time -0.0281 0.000 -211.081 0.000 -0.028 -0.028 ccode_461_time -0.0052 0.000 -42.688 0.000 -0.005 -0.005 ccode_471_time -0.0106 8.85e-05 -119.747 0.000 -0.011 -0.010 ccode_475_time 0.0002 0.000 1.308 0.191 -0.000 0.001 ccode_481_time -0.0005 0.000 -2.518 0.012 -0.001 -0.000 ccode_482_time 4.412e-05 4.9e-05 0.901 0.368 -5.19e-05 0.000 ccode_483_time -0.0185 0.001 -31.860 0.000 -0.020 -0.017 ccode_484_time 0.0415 0.000 162.672 0.000 0.041 0.042 ccode_490_time -0.0379 0.000 -88.293 0.000 -0.039 -0.037 ccode_500_time -0.0095 0.000 -25.149 0.000 -0.010 -0.009 ccode_501_time -0.0133 0.000 -45.306 0.000 -0.014 -0.013 ccode_510_time -3.845e-05 2.79e-05 -1.376 0.169 -9.32e-05 1.63e-05 ccode_516_time 0.0662 0.000 249.253 0.000 0.066 0.067 ccode_517_time 0.0697 0.000 244.485 0.000 0.069 0.070 ccode_520_time -0.0015 0.002 -0.705 0.481 -0.006 0.003 ccode_522_time 0.0437 0.001 30.559 0.000 0.041 0.047 ccode_530_time -0.0246 6.17e-05 -398.471 0.000 -0.025 -0.024 ccode_540_time -0.0004 0.000 -2.421 0.015 -0.001 -6.67e-05 ccode_541_time -0.0737 0.000 -337.720 0.000 -0.074 -0.073 ccode_551_time 5.597e-05 5.52e-05 1.014 0.310 -5.22e-05 0.000 ccode_552_time 0.0301 0.000 159.587 0.000 0.030 0.030 ccode_553_time 7.894e-05 0.000 0.257 0.797 -0.001 0.001 ccode_560_time -0.0684 5.73e-05 -1192.513 0.000 -0.068 -0.068 ccode_565_time 0.1134 0.001 80.950 0.000 0.111 0.116 ccode_570_time 0.0142 0.000 100.113 0.000 0.014 0.014 ccode_571_time 0.0002 0.000 1.506 0.132 -6.68e-05 0.001 ccode_572_time -0.0001 5.78e-05 -2.521 0.012 -0.000 -3.24e-05 ccode_580_time -0.0002 0.000 -1.087 0.277 -0.001 0.000 ccode_625_time 0.0331 3.67e-05 901.316 0.000 0.033 0.033 ============================================================================== Omnibus: 91.156 Durbin-Watson: 1.478 Prob(Omnibus): 0.000 Jarque-Bera (JB): 421.027 Skew: 0.452 Prob(JB): 3.76e-92 Kurtosis: 6.575 Cond. No. 226. ============================================================================== Notes: [1] Standard Errors are robust to cluster correlation (cluster) 0.24

Modelo 2 

y = data['war_prio']

# add constant

X = sm.add_constant(data.loc[:,["GPCP_g", "GPCP_g_l"]])

# sm function

ols_model2 = sm.OLS(y, X).fit()

# fit() permite correr la regresión

print(ols_model2.summary())

# Robust standar error

ols_model2_rb = sm.OLS(y, X).fit(cov_type = "HC1")
print(ols_model2_rb.summary())
OLS Regression Results ============================================================================== Dep. Variable: war_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.001 Method: Least Squares F-statistic: 1.200 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.302 Time: 20:44:51 Log-Likelihood: -320.10 No. Observations: 743 AIC: 646.2 Df Residuals: 740 BIC: 660.0 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 0.1697 0.014 12.292 0.000 0.143 0.197 GPCP_g -0.0977 0.072 -1.363 0.173 -0.238 0.043 GPCP_g_l -0.0891 0.073 -1.228 0.220 -0.232 0.053 ============================================================================== Omnibus: 216.896 Durbin-Watson: 0.482 Prob(Omnibus): 0.000 Jarque-Bera (JB): 434.329 Skew: 1.777 Prob(JB): 4.86e-95 Kurtosis: 4.181 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. OLS Regression Results ============================================================================== Dep. Variable: war_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.001 Method: Least Squares F-statistic: 1.507 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.222 Time: 20:44:51 Log-Likelihood: -320.10 No. Observations: 743 AIC: 646.2 Df Residuals: 740 BIC: 660.0 Df Model: 2 Covariance Type: HC1 ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ const 0.1697 0.014 12.142 0.000 0.142 0.197 GPCP_g -0.0977 0.066 -1.474 0.140 -0.228 0.032 GPCP_g_l -0.0891 0.063 -1.412 0.158 -0.213 0.035 ============================================================================== Omnibus: 216.896 Durbin-Watson: 0.482 Prob(Omnibus): 0.000 Jarque-Bera (JB): 434.329 Skew: 1.777 Prob(JB): 4.86e-95 Kurtosis: 4.181 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors are heteroscedasticity robust (HC1) 
#alternative robust standar error
ols_model_rb2 = sm.OLS(y, X).fit(cov_type = "HC1") # Huber-White robust se

print(ols_model_rb2.summary())
OLS Regression Results ============================================================================== Dep. Variable: war_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.001 Method: Least Squares F-statistic: 1.507 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.222 Time: 20:45:03 Log-Likelihood: -320.10 No. Observations: 743 AIC: 646.2 Df Residuals: 740 BIC: 660.0 Df Model: 2 Covariance Type: HC1 ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ const 0.1697 0.014 12.142 0.000 0.142 0.197 GPCP_g -0.0977 0.066 -1.474 0.140 -0.228 0.032 GPCP_g_l -0.0891 0.063 -1.412 0.158 -0.213 0.035 ============================================================================== Omnibus: 216.896 Durbin-Watson: 0.482 Prob(Omnibus): 0.000 Jarque-Bera (JB): 434.329 Skew: 1.777 Prob(JB): 4.86e-95 Kurtosis: 4.181 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors are heteroscedasticity robust (HC1) 
# Acceder a la información de la tabla

ols_model_rb2.summary2()

ols_model_rb2.summary2().tables[1]

dir(sm.OLS(y, X))

# Lista de atributos y métodos
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_check_kwargs', '_data_attr', '_df_model', '_df_resid', '_fit_collinear', '_fit_ridge', '_fit_zeros', '_formula_max_endog', '_get_init_kwds', '_handle_data', '_init_keys', '_kwargs_allowed', '_setup_score_hess', '_sqrt_lasso', 'data', 'df_model', 'df_resid', 'endog', 'endog_names', 'exog', 'exog_names', 'fit', 'fit_regularized', 'from_formula', 'get_distribution', 'hessian', 'hessian_factor', 'information', 'initialize', 'k_constant', 'loglike', 'nobs', 'predict', 'rank', 'score', 'weights', 'wendog', 'wexog', 'whiten']
# Y estimados a partir del método predict 

sm.OLS(y, X).fit().predict()
array([0.18704291, 0.16156761, 0.1591636 , 0.16689364, 0.16611632, 0.15020309, 0.16052623, 0.16445212, 0.1678748 , 0.17493067, 0.18461317, 0.21096717, 0.14646171, 0.14220873, 0.18236471, 0.17711868, 0.18211489, 0.18210699, 0.14566715, 0.17998848, 0.16871667, 0.17702256, 0.16698367, 0.15666318, 0.1680248 , 0.17984615, 0.16204504, 0.1430644 , 0.1802974 , 0.14964679, 0.14789666, 0.18521695, 0.15446167, 0.16866698, 0.18319467, 0.17212033, 0.15998443, 0.16219266, 0.14809908, 0.19410448, 0.18898141, 0.18438161, 0.1734193 , 0.14512834, 0.17947203, 0.1080412 , 0.11027748, 0.21478754, 0.16391527, 0.1845413 , 0.13976727, 0.11104919, 0.16801464, 0.14509102, 0.14936252, 0.18708431, 0.19845518, 0.17957136, 0.17248373, 0.18268706, 0.1702437 , 0.14207546, 0.15359244, 0.1777945 , 0.16454007, 0.15436827, 0.186385 , 0.15436469, 0.14794209, 0.18320195, 0.15511713, 0.166916 , 0.18187508, 0.16644452, 0.16207225, 0.15871311, 0.17390356, 0.13542463, 0.16609041, 0.1940256 , 0.15327744, 0.16152694, 0.17701248, 0.15333377, 0.15588651, 0.18178009, 0.18544734, 0.18102702, 0.19238042, 0.17812424, 0.16287117, 0.16802763, 0.16162038, 0.17802261, 0.16563186, 0.17415717, 0.16759646, 0.18110427, 0.17467989, 0.14947877, 0.1714517 , 0.18359009, 0.16247644, 0.16628072, 0.16164332, 0.1636782 , 0.17512774, 0.16253175, 0.16759689, 0.17313232, 0.16962104, 0.16977008, 0.17919028, 0.15981144, 0.16498076, 0.16277749, 0.17780929, 0.18909981, 0.15799448, 0.15521208, 0.17201592, 0.16114667, 0.15991444, 0.16695022, 0.16935811, 0.17699674, 0.17946938, 0.16566712, 0.16434882, 0.17016698, 0.17781272, 0.16502038, 0.15318973, 0.16544964, 0.19148407, 0.18787251, 0.20031107, 0.15687086, 0.14979529, 0.18722649, 0.13143828, 0.14516687, 0.21543774, 0.15845881, 0.12520563, 0.18152704, 0.12671072, 0.13232035, 0.18971026, 0.18393917, 0.16208728, 0.14271711, 0.14576751, 0.16000941, 0.18016869, 0.16290845, 0.14075029, 0.18143644, 0.18127924, 0.1526682 , 0.1646786 , 0.1798863 , 0.18942011, 0.17439827, 0.15942248, 0.1672099 , 0.16370274, 0.17189004, 0.18657038, 0.16575548, 0.14826875, 0.13594103, 0.13915283, 0.18840088, 0.20532517, 0.16983948, 0.15135654, 0.17843133, 0.1520727 , 0.14061413, 0.19234703, 0.21477279, 0.15514228, 0.15207831, 0.18710926, 0.20282314, 0.16165132, 0.14981917, 0.16472176, 0.15445135, 0.16228788, 0.18100661, 0.19059567, 0.16938383, 0.13874985, 0.15727079, 0.18267049, 0.13946622, 0.15154327, 0.19780696, 0.18044045, 0.15052487, 0.1614658 , 0.18290449, 0.15501597, 0.13844977, 0.17849413, 0.18040646, 0.15855663, 0.16498806, 0.18514041, 0.18950848, 0.17160514, 0.15637973, 0.16720823, 0.16970888, 0.16894229, 0.18159817, 0.16500872, 0.14367771, 0.16136921, 0.15740701, 0.20200474, 0.17496123, 0.14547437, 0.13512712, 0.14491251, 0.15345414, 0.16504363, 0.20073796, 0.18862624, 0.1619266 , 0.15739639, 0.14600772, 0.17220029, 0.18415935, 0.17434085, 0.17551435, 0.14825508, 0.18491573, 0.18730939, 0.1945623 , 0.14339232, 0.12653114, 0.17440473, 0.16309447, 0.16725978, 0.16760426, 0.17442337, 0.15696866, 0.17266447, 0.18704612, 0.17229268, 0.16373636, 0.1627915 , 0.17283451, 0.18546656, 0.14741111, 0.16374303, 0.1708573 , 0.20133781, 0.18597484, 0.15978412, 0.14547983, 0.15475955, 0.16405312, 0.15975333, 0.1710791 , 0.1670655 , 0.16449794, 0.17809304, 0.1619682 , 0.17368958, 0.19080806, 0.18097444, 0.16895817, 0.17033488, 0.15735606, 0.1521995 , 0.18861119, 0.17922437, 0.15908425, 0.16268817, 0.144761 , 0.14191308, 0.16210217, 0.19210516, 0.17207626, 0.16141921, 0.15482731, 0.16221672, 0.17979104, 0.16770973, 0.17097229, 0.17857576, 0.15472526, 0.18194385, 0.17270062, 0.1920322 , 0.15509649, 0.13716635, 0.17635244, 0.16594548, 0.17102634, 0.16738002, 0.17410421, 0.17367534, 0.17236854, 0.17645524, 0.16244065, 0.16286941, 0.17662267, 0.18396776, 0.17370455, 0.16101548, 0.16923238, 0.11190001, 0.17801593, 0.20261344, 0.15289799, 0.16481666, 0.19364133, 0.16133588, 0.1467095 , 0.17121662, 0.19580919, 0.18360753, 0.17473608, 0.15650069, 0.1570425 , 0.19069279, 0.09478098, 0.11662815, 0.22025171, 0.15045632, 0.1672967 , 0.18178798, 0.16524338, 0.16360239, 0.15097496, 0.14773611, 0.14540589, 0.17326466, 0.20525889, 0.16924749, 0.17906121, 0.15295605, 0.14632107, 0.16156667, 0.12915442, 0.1654211 , 0.18161083, 0.18346956, 0.1738644 , 0.16963028, 0.1964992 , 0.16736664, 0.15501128, 0.16811332, 0.15880366, 0.16602904, 0.1613465 , 0.1844751 , 0.19097083, 0.1609754 , 0.1533545 , 0.17307076, 0.17343374, 0.17810026, 0.17139475, 0.17215015, 0.17654417, 0.14518327, 0.18024761, 0.180942 , 0.16359373, 0.167556 , 0.15599243, 0.16161041, 0.17353556, 0.17738658, 0.16938193, 0.18410675, 0.19667618, 0.16255364, 0.16037175, 0.1678773 , 0.14420727, 0.17099655, 0.20556127, 0.16805829, 0.11572445, 0.18159303, 0.1800722 , 0.18210499, 0.16185073, 0.15424136, 0.17488568, 0.14798629, 0.15497048, 0.19050428, 0.14164697, 0.17298674, 0.19172098, 0.19318605, 0.17714965, 0.15039347, 0.14711426, 0.18074995, 0.14971273, 0.14660418, 0.1978318 , 0.16626958, 0.1476653 , 0.17641938, 0.13227487, 0.15285324, 0.19920126, 0.1795625 , 0.14991386, 0.13920319, 0.16828129, 0.19342648, 0.20616132, 0.16583065, 0.14727692, 0.15805624, 0.18741266, 0.15220377, 0.12490523, 0.20183799, 0.20601325, 0.17341799, 0.13514278, 0.09431298, 0.14184593, 0.20374632, 0.1962324 , 0.16160206, 0.12980427, 0.16257447, 0.17328315, 0.18290921, 0.15760305, 0.14187327, 0.17081467, 0.20606595, 0.15731004, 0.12554287, 0.18902469, 0.18770634, 0.19027183, 0.1625395 , 0.16043646, 0.17314178, 0.13489106, 0.14129788, 0.18517464, 0.15821201, 0.15011158, 0.21350717, 0.05465512, 0.0476425 , 0.21957367, 0.17853394, 0.10087474, 0.16877245, 0.16398743, 0.18667498, 0.19340881, 0.18671646, 0.19575813, 0.15283282, 0.15527513, 0.21186909, 0.13221655, 0.12165224, 0.21494396, 0.14650931, 0.1253552 , 0.18558154, 0.0949594 , 0.12566243, 0.20696712, 0.17983861, 0.13608168, 0.13428499, 0.1836093 , 0.17832032, 0.18238577, 0.17625905, 0.15436404, 0.16265776, 0.1776055 , 0.15578372, 0.1503598 , 0.17238686, 0.16816736, 0.16873778, 0.1731356 , 0.15981054, 0.15953081, 0.17398574, 0.17258451, 0.17598569, 0.16465887, 0.17446053, 0.13876987, 0.16702164, 0.18797323, 0.15064069, 0.17067385, 0.17244879, 0.13928198, 0.16346391, 0.18397256, 0.18568486, 0.18871859, 0.1804116 , 0.16400864, 0.15943395, 0.16632276, 0.17061527, 0.18057733, 0.17157088, 0.1635388 , 0.1588517 , 0.20205154, 0.17604352, 0.14225616, 0.13985979, 0.15100566, 0.15188 , 0.16080549, 0.20309118, 0.19244664, 0.16293896, 0.15537597, 0.14626345, 0.17022956, 0.18261636, 0.17942167, 0.17431904, 0.14521316, 0.1663386 , 0.16542766, 0.19448551, 0.18572249, 0.1653653 , 0.14639544, 0.15099438, 0.17556209, 0.16852706, 0.1719489 , 0.17247756, 0.15936575, 0.16994439, 0.17545043, 0.17752014, 0.18563946, 0.19237074, 0.17882417, 0.1778929 , 0.12045619, 0.07140599, 0.20074209, 0.17459829, 0.11976406, 0.21110232, 0.12880444, 0.10420644, 0.15247391, 0.15697276, 0.21193017, 0.14725273, 0.17320624, 0.19506753, 0.17539197, 0.15001762, 0.16083143, 0.17959224, 0.1415976 , 0.15742811, 0.20271294, 0.16581939, 0.17720555, 0.15955643, 0.1487012 , 0.15165633, 0.13070567, 0.172528 , 0.19210238, 0.18560423, 0.16678886, 0.18539161, 0.18971351, 0.19536676, 0.14363159, 0.13098983, 0.16664983, 0.14535142, 0.15745466, 0.19991597, 0.17942568, 0.15175086, 0.15889733, 0.16164316, 0.16464185, 0.1480495 , 0.17522556, 0.20085826, 0.14102674, 0.18947149, 0.17171226, 0.10198957, 0.16606492, 0.21912074, 0.16239543, 0.14603957, 0.17041403, 0.18964808, 0.1902954 , 0.20268902, 0.12760449, 0.12630954, 0.15566774, 0.12359586, 0.16186551, 0.1942139 , 0.17708252, 0.19659289, 0.14613178, 0.15557385, 0.17597748, 0.15817872, 0.17623212, 0.18785079, 0.17045474, 0.14378938, 0.17200213, 0.19065048, 0.17862203, 0.18883183, 0.17775083, 0.15466109, 0.17126419, 0.1582525 , 0.1692128 , 0.17515346, 0.18632771, 0.18868285, 0.18903063, 0.14350925, 0.13653112, 0.18037886, 0.1704386 , 0.15197 , 0.16331233, 0.18516054, 0.15446659, 0.17385424, 0.1824608 , 0.15719475, 0.15794858, 0.1675651 , 0.17413515, 0.18333956, 0.15955835, 0.16448492, 0.13434514, 0.1714557 , 0.20562819, 0.14524184, 0.14775105, 0.17944519, 0.13930323, 0.1589058 , 0.18089702, 0.16790272, 0.18130923, 0.19010938, 0.17093998, 0.16493286, 0.1616061 , 0.16070747, 0.19104145, 0.1677181 , 0.17148525, 0.15273654, 0.1693474 , 0.18458853, 0.1514887 , 0.16379162, 0.1711832 , 0.1494963 , 0.15369807, 0.176016 , 0.18770166, 0.19434745, 0.17849891, 0.16640218, 0.16838233, 0.16935242, 0.18324291, 0.17970368, 0.18651227, 0.1660176 , 0.17194037, 0.17286607, 0.15804757, 0.15688771, 0.18808021, 0.17347543, 0.13777878, 0.1739309 , 0.18251431, 0.19563886, 0.15242018, 0.1564431 , 0.19382495, 0.15894914, 0.15679604, 0.18238104, 0.16721841, 0.13976083, 0.18681281, 0.2026636 , 0.1576839 , 0.12280629, 0.14468015, 0.20554201, 0.15639431, 0.14328271, 0.18410435, 0.19104338, 0.2008204 , 0.1317299 , 0.15190923, 0.19194582, 0.11405117, 0.11955249, 0.1834425 , 0.17662792])
# Recordad métodos y atributos 

print(dir(ols_model2))

# predict para ello uso la función predict 

ols_model2.predict(X)

# acceso a los parámetros

ols_model2.params

# R2 y R2  ajustado

ols_model2.rsquared
ols_model2.rsquared_adj
['HC0_se', 'HC1_se', 'HC2_se', 'HC3_se', '_HCCM', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_abat_diagonal', '_cache', '_data_attr', '_data_in_cache', '_get_robustcov_results', '_is_nested', '_use_t', '_wexog_singular_values', 'aic', 'bic', 'bse', 'centered_tss', 'compare_f_test', 'compare_lm_test', 'compare_lr_test', 'condition_number', 'conf_int', 'conf_int_el', 'cov_HC0', 'cov_HC1', 'cov_HC2', 'cov_HC3', 'cov_kwds', 'cov_params', 'cov_type', 'df_model', 'df_resid', 'diagn', 'eigenvals', 'el_test', 'ess', 'f_pvalue', 'f_test', 'fittedvalues', 'fvalue', 'get_influence', 'get_prediction', 'get_robustcov_results', 'info_criteria', 'initialize', 'k_constant', 'llf', 'load', 'model', 'mse_model', 'mse_resid', 'mse_total', 'nobs', 'normalized_cov_params', 'outlier_test', 'params', 'predict', 'pvalues', 'remove_data', 'resid', 'resid_pearson', 'rsquared', 'rsquared_adj', 'save', 'scale', 'ssr', 'summary', 'summary2', 't_test', 't_test_pairwise', 'tvalues', 'uncentered_tss', 'use_t', 'wald_test', 'wald_test_terms', 'wresid']
0.0005381642535063902
control_formula = "war_prio"+ " ~ "+ "GPCP_g + " + "GPCP_g_l"

ols_model2 = smf.ols(control_formula, data=data).fit()

print(ols_model2.summary())
OLS Regression Results ============================================================================== Dep. Variable: war_prio R-squared: 0.003 Model: OLS Adj. R-squared: 0.001 Method: Least Squares F-statistic: 1.200 Date: Sat, 10 Dec 2022 Prob (F-statistic): 0.302 Time: 20:45:57 Log-Likelihood: -320.10 No. Observations: 743 AIC: 646.2 Df Residuals: 740 BIC: 660.0 Df Model: 2 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 0.1697 0.014 12.292 0.000 0.143 0.197 GPCP_g -0.0977 0.072 -1.363 0.173 -0.238 0.043 GPCP_g_l -0.0891 0.073 -1.228 0.220 -0.232 0.053 ============================================================================== Omnibus: 216.896 Durbin-Watson: 0.482 Prob(Omnibus): 0.000 Jarque-Bera (JB): 434.329 Skew: 1.777 Prob(JB): 4.86e-95 Kurtosis: 4.181 Cond. No. 6.26 ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. 
ols_model2_skl = linear_model.LinearRegression().fit( X, y )

ols_model2_skl.coef_ # acceso a coeficientes 

ols_model2_skl.predict(X) # predict en formato array 
ols_model2_skl.score(X,y) # R cuadrado
dir(ols_model2_skl)
mean_squared_error( y, ols_model2.predict())**0.5 # root mean square error
0.37227542395527624
formula_model2 = "war_prio ~ GPCP_g + GPCP_g_l + C(ccode)" + ' + ' + ' + '.join( country_trend )

ols_model2 = smf.ols(formula_model2, data=data).fit(cov_type='cluster', cov_kwds={'groups': data['ccode']})

print(ols_model2.summary())

rmse_ol2 = round(mean_squared_error( y, ols_model2.predict())**0.5,2)

print(rmse_ol2)
OLS Regression Results ============================================================================== Dep. Variable: war_prio R-squared: 0.699 Model: OLS Adj. R-squared: 0.661 Method: Least Squares F-statistic: 335.5 Date: Sat, 10 Dec 2022 Prob (F-statistic): 1.01e-25 Time: 20:46:12 Log-Likelihood: 124.86 No. Observations: 743 AIC: -81.72 Df Residuals: 659 BIC: 305.6 Df Model: 83 Covariance Type: cluster ===================================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------------- Intercept -0.1101 0.002 -45.002 0.000 -0.115 -0.105 C(ccode)[T.420.0] 0.1149 0.001 199.754 0.000 0.114 0.116 C(ccode)[T.432.0] 0.1033 0.005 19.235 0.000 0.093 0.114 C(ccode)[T.433.0] -0.0023 0.001 -2.213 0.027 -0.004 -0.000 C(ccode)[T.434.0] 0.1096 0.003 40.431 0.000 0.104 0.115 C(ccode)[T.435.0] 0.1019 0.006 16.920 0.000 0.090 0.114 C(ccode)[T.436.0] 0.0952 0.009 10.702 0.000 0.078 0.113 C(ccode)[T.437.0] 0.1097 0.003 41.518 0.000 0.104 0.115 C(ccode)[T.438.0] -0.0040 0.002 -2.267 0.023 -0.008 -0.001 C(ccode)[T.439.0] 0.1620 0.003 58.445 0.000 0.157 0.167 C(ccode)[T.450.0] -0.0897 0.002 -36.815 0.000 -0.094 -0.085 C(ccode)[T.451.0] -0.1389 0.001 -129.586 0.000 -0.141 -0.137 C(ccode)[T.452.0] 0.1090 0.003 36.791 0.000 0.103 0.115 C(ccode)[T.461.0] 0.1085 0.003 34.498 0.000 0.102 0.115 C(ccode)[T.471.0] 0.1095 0.003 39.909 0.000 0.104 0.115 C(ccode)[T.475.0] 0.1073 0.004 29.411 0.000 0.100 0.114 C(ccode)[T.481.0] 0.1145 0.001 117.993 0.000 0.113 0.116 C(ccode)[T.482.0] 0.1105 0.002 48.065 0.000 0.106 0.115 C(ccode)[T.483.0] 1.2499 0.006 216.523 0.000 1.239 1.261 C(ccode)[T.484.0] -0.2316 0.001 -272.071 0.000 -0.233 -0.230 C(ccode)[T.490.0] 1.3097 0.001 2311.875 0.000 1.309 1.311 C(ccode)[T.500.0] 1.3184 0.001 1150.994 0.000 1.316 1.321 C(ccode)[T.501.0] 0.1104 0.003 38.947 0.000 0.105 0.116 C(ccode)[T.510.0] 0.1097 0.003 41.535 0.000 0.105 0.115 C(ccode)[T.516.0] -0.0009 0.000 -2.207 0.027 -0.002 -0.000 C(ccode)[T.517.0] -0.1895 0.000 -603.755 0.000 -0.190 -0.189 C(ccode)[T.520.0] -0.4516 0.015 -30.400 0.000 -0.481 -0.423 C(ccode)[T.522.0] 0.1279 0.006 23.040 0.000 0.117 0.139 C(ccode)[T.530.0] 1.6181 0.001 1209.520 0.000 1.615 1.621 C(ccode)[T.540.0] 1.2389 0.001 890.450 0.000 1.236 1.242 C(ccode)[T.541.0] 1.6248 0.003 537.619 0.000 1.619 1.631 C(ccode)[T.551.0] 0.1087 0.003 35.587 0.000 0.103 0.115 C(ccode)[T.552.0] -0.1417 0.002 -67.737 0.000 -0.146 -0.138 C(ccode)[T.553.0] 0.1072 0.004 28.201 0.000 0.100 0.115 C(ccode)[T.560.0] 1.6180 0.001 1190.509 0.000 1.615 1.621 C(ccode)[T.565.0] -1.6018 0.019 -83.329 0.000 -1.639 -1.564 C(ccode)[T.570.0] 0.1155 0.001 121.781 0.000 0.114 0.117 C(ccode)[T.571.0] 0.1122 0.002 72.342 0.000 0.109 0.115 C(ccode)[T.572.0] 0.1146 0.001 215.878 0.000 0.114 0.116 C(ccode)[T.580.0] 0.1135 0.001 107.970 0.000 0.111 0.116 C(ccode)[T.625.0] 0.6990 0.002 346.178 0.000 0.695 0.703 GPCP_g -0.0625 0.030 -2.088 0.037 -0.121 -0.004 GPCP_g_l -0.0687 0.032 -2.174 0.030 -0.131 -0.007 ccode_404_time 0.0138 0.000 136.685 0.000 0.014 0.014 ccode_420_time -0.0002 7.58e-05 -2.310 0.021 -0.000 -2.66e-05 ccode_432_time 0.0008 0.000 2.309 0.021 0.000 0.001 ccode_433_time 0.0139 3.9e-05 357.734 0.000 0.014 0.014 ccode_434_time 0.0002 9.92e-05 2.229 0.026 2.67e-05 0.000 ccode_435_time 0.0009 0.000 2.294 0.022 0.000 0.002 ccode_436_time 0.0017 0.001 2.316 0.021 0.000 0.003 ccode_437_time -7.637e-06 3.43e-06 -2.227 0.026 -1.44e-05 -9.15e-07 ccode_438_time 0.0139 7.64e-05 181.539 0.000 0.014 0.014 ccode_439_time 0.0003 0.000 2.314 0.021 3.88e-05 0.000 ccode_450_time 0.0361 0.000 315.758 0.000 0.036 0.036 ccode_451_time 0.0294 0.000 139.810 0.000 0.029 0.030 ccode_452_time 0.0001 8.65e-05 1.619 0.105 -2.95e-05 0.000 ccode_461_time 0.0002 8.42e-05 2.132 0.033 1.45e-05 0.000 ccode_471_time 8.159e-05 5.6e-05 1.457 0.145 -2.82e-05 0.000 ccode_475_time 0.0003 0.000 2.270 0.023 3.68e-05 0.001 ccode_481_time -0.0002 0.000 -1.828 0.068 -0.000 1.41e-05 ccode_482_time 7.448e-05 3.41e-05 2.186 0.029 7.7e-06 0.000 ccode_483_time -0.0465 0.000 -116.348 0.000 -0.047 -0.046 ccode_484_time 0.0418 0.000 275.958 0.000 0.041 0.042 ccode_490_time -0.0512 0.000 -178.531 0.000 -0.052 -0.051 ccode_500_time -0.0479 0.000 -192.114 0.000 -0.048 -0.047 ccode_501_time 0.0002 0.000 1.443 0.149 -8.18e-05 0.001 ccode_510_time -4.393e-05 1.93e-05 -2.281 0.023 -8.17e-05 -6.18e-06 ccode_516_time 0.0136 0.000 77.533 0.000 0.013 0.014 ccode_517_time 0.0469 0.000 242.507 0.000 0.047 0.047 ccode_520_time 0.1060 0.001 73.516 0.000 0.103 0.109 ccode_522_time -0.0022 0.001 -2.253 0.024 -0.004 -0.000 ccode_530_time -0.0773 4.27e-05 -1808.402 0.000 -0.077 -0.077 ccode_540_time -0.0195 8.62e-05 -225.947 0.000 -0.020 -0.019 ccode_541_time -0.0734 0.000 -497.796 0.000 -0.074 -0.073 ccode_551_time 8.487e-05 3.84e-05 2.213 0.027 9.7e-06 0.000 ccode_552_time 0.0301 0.000 231.268 0.000 0.030 0.030 ccode_553_time 0.0004 0.000 1.989 0.047 6.07e-06 0.001 ccode_560_time -0.0685 3.55e-05 -1928.554 0.000 -0.069 -0.068 ccode_565_time 0.1148 0.001 134.450 0.000 0.113 0.116 ccode_570_time -0.0001 8.9e-05 -1.419 0.156 -0.000 4.81e-05 ccode_571_time 0.0002 0.000 2.298 0.022 3.41e-05 0.000 ccode_572_time -4.777e-05 3.14e-05 -1.524 0.128 -0.000 1.37e-05 ccode_580_time -0.0003 0.000 -2.229 0.026 -0.000 -3.09e-05 ccode_625_time 0.0165 1.97e-05 840.404 0.000 0.016 0.017 ============================================================================== Omnibus: 138.515 Durbin-Watson: 1.311 Prob(Omnibus): 0.000 Jarque-Bera (JB): 1043.072 Skew: 0.609 Prob(JB): 3.16e-227 Kurtosis: 8.675 Cond. No. 226. ============================================================================== Notes: [1] Standard Errors are robust to cluster correlation (cluster) 0.2 
# Lista de explicativa a mostrarse en la tabla

explicativas = ['GPCP_g','GPCP_g_l']

# etiquetas a las variables 

etiquetas = ['Growth in rainfall, t','Growth in rainfall, t-1']


labels = dict(zip(explicativas,etiquetas))
labels
{'GPCP_g': 'Growth in rainfall, t', 'GPCP_g_l': 'Growth in rainfall, t-1'}
pystout(models = [ols_model1,ols_model2], file='regression_table.tex', digits=3,
        endog_names=['Civil Conflict 25 Deaths (OLS)','Civil Conflict 1,000 Deaths'],
        exogvars =explicativas ,  # sellecionamos las variables 
        varlabels = labels,  # etiquetas a las variables
        mgroups={'Ordinary Least Squares':[1,5]}, # titulo a las regresiones
        modstat={'nobs':'Observarions','rsquared':'R\sym{2}'}, # estadísticos 
        addrows={'Country fixed effects':['yes','yes'], 'Country-specific time trends' :
                 ['yes','yes'],
                 'Root mean square error': [rmse_ol1,rmse_ol2]}, # añadimos filas 
        addnotes=['Note.—Huber robust standard errors are in parentheses.',
                  'Regression disturbance terms are clustered at the country level.',
                 'A country-specific year time trend is included in all specifications (coefficient estimates not reported).',
                 '* Significantly different from zero at 90 percent confidence.',
                 '** Significantly different from zero at 95 percent confidence.',
                 '* Significantly different from zero at 99 percent confidence.'],
        title='Rainfall and Economic Growth',
        stars={.1:'',.05:'',.01:'**'}
       )

Coeficient Plox

import pandas as pd
import numpy as np

#plots library

import matplotlib.pyplot as plt
import seaborn as sns

# import linear models library

import statsmodels.api as sm
import statsmodels.formula.api as smf

Modelo 1

formula_model1 = "any_prio ~ GPCP_g + GPCP_g_l + C(ccode)" + ' + ' + ' + '.join( country_trend )

ols_model1 = smf.ols(formula_model1, data=data).fit(cov_type='cluster', cov_kwds={'groups': data['ccode']})

print(ols_model1.summary())
OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.708 Model: OLS Adj. R-squared: 0.672 Method: Least Squares F-statistic: 162.4 Date: Sat, 10 Dec 2022 Prob (F-statistic): 6.28e-20 Time: 20:47:45 Log-Likelihood: 8.6565 No. Observations: 743 AIC: 150.7 Df Residuals: 659 BIC: 538.0 Df Model: 83 Covariance Type: cluster ===================================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------------- Intercept -0.2458 0.004 -66.965 0.000 -0.253 -0.239 C(ccode)[T.420.0] 0.4921 0.001 468.956 0.000 0.490 0.494 C(ccode)[T.432.0] 0.2600 0.008 32.651 0.000 0.244 0.276 C(ccode)[T.433.0] -0.0986 0.002 -59.007 0.000 -0.102 -0.095 C(ccode)[T.434.0] 0.2438 0.004 57.888 0.000 0.236 0.252 C(ccode)[T.435.0] 0.2377 0.009 26.715 0.000 0.220 0.255 C(ccode)[T.436.0] 0.1243 0.013 9.406 0.000 0.098 0.150 C(ccode)[T.437.0] 0.2450 0.004 61.386 0.000 0.237 0.253 C(ccode)[T.438.0] -0.0061 0.003 -2.184 0.029 -0.012 -0.001 C(ccode)[T.439.0] 0.4443 0.004 105.272 0.000 0.436 0.453 C(ccode)[T.450.0] -0.3559 0.004 -92.197 0.000 -0.363 -0.348 C(ccode)[T.451.0] -0.2257 0.002 -120.766 0.000 -0.229 -0.222 C(ccode)[T.452.0] 0.6882 0.004 159.022 0.000 0.680 0.697 C(ccode)[T.461.0] 0.4129 0.005 88.451 0.000 0.404 0.422 C(ccode)[T.471.0] 0.4257 0.004 106.398 0.000 0.418 0.433 C(ccode)[T.475.0] 0.2433 0.005 44.966 0.000 0.233 0.254 C(ccode)[T.481.0] 0.2542 0.002 162.114 0.000 0.251 0.257 C(ccode)[T.482.0] 0.2466 0.003 72.125 0.000 0.240 0.253 C(ccode)[T.483.0] 1.3664 0.008 162.199 0.000 1.350 1.383 C(ccode)[T.484.0] -0.0916 0.002 -58.163 0.000 -0.095 -0.088 C(ccode)[T.490.0] 1.3481 0.001 1581.192 0.000 1.346 1.350 C(ccode)[T.500.0] 1.2554 0.002 743.377 0.000 1.252 1.259 C(ccode)[T.501.0] 0.4613 0.005 92.925 0.000 0.452 0.471 C(ccode)[T.510.0] 0.2446 0.004 60.659 0.000 0.237 0.252 C(ccode)[T.516.0] -0.1279 0.001 -184.968 0.000 -0.129 -0.127 C(ccode)[T.517.0] -0.1171 0.001 -198.672 0.000 -0.118 -0.116 C(ccode)[T.520.0] 1.2725 0.021 59.568 0.000 1.231 1.314 C(ccode)[T.522.0] -0.0132 0.008 -1.660 0.097 -0.029 0.002 C(ccode)[T.530.0] 1.3320 0.002 620.380 0.000 1.328 1.336 C(ccode)[T.540.0] 1.2494 0.002 611.501 0.000 1.245 1.253 C(ccode)[T.541.0] 1.7636 0.004 405.215 0.000 1.755 1.772 C(ccode)[T.551.0] 0.2440 0.005 53.114 0.000 0.235 0.253 C(ccode)[T.552.0] -0.0049 0.003 -1.585 0.113 -0.011 0.001 C(ccode)[T.553.0] 0.2460 0.005 44.830 0.000 0.235 0.257 C(ccode)[T.560.0] 1.7528 0.002 796.854 0.000 1.749 1.757 C(ccode)[T.565.0] -1.4419 0.030 -47.517 0.000 -1.501 -1.382 C(ccode)[T.570.0] 0.1329 0.002 74.355 0.000 0.129 0.136 C(ccode)[T.571.0] 0.2493 0.002 110.308 0.000 0.245 0.254 C(ccode)[T.572.0] 0.2519 0.001 329.982 0.000 0.250 0.253 C(ccode)[T.580.0] 0.2488 0.002 147.237 0.000 0.245 0.252 C(ccode)[T.625.0] 0.7557 0.003 246.088 0.000 0.750 0.762 GPCP_g -0.0238 0.043 -0.550 0.582 -0.108 0.061 GPCP_g_l -0.1219 0.052 -2.352 0.019 -0.224 -0.020 ccode_404_time 0.0295 0.000 185.142 0.000 0.029 0.030 ccode_420_time -0.0160 0.000 -143.094 0.000 -0.016 -0.016 ccode_432_time 0.0078 0.001 15.618 0.000 0.007 0.009 ccode_433_time 0.0595 5.87e-05 1014.282 0.000 0.059 0.060 ccode_434_time 0.0004 0.000 2.272 0.023 4.97e-05 0.001 ccode_435_time 0.0009 0.001 1.469 0.142 -0.000 0.002 ccode_436_time 0.0369 0.001 34.562 0.000 0.035 0.039 ccode_437_time -1.254e-05 5.51e-06 -2.276 0.023 -2.33e-05 -1.74e-06 ccode_438_time 0.0297 0.000 270.544 0.000 0.030 0.030 ccode_439_time -0.0032 0.000 -19.303 0.000 -0.004 -0.003 ccode_450_time 0.1090 0.000 656.335 0.000 0.109 0.109 ccode_451_time 0.0786 0.000 259.266 0.000 0.078 0.079 ccode_452_time -0.0281 0.000 -211.081 0.000 -0.028 -0.028 ccode_461_time -0.0052 0.000 -42.688 0.000 -0.005 -0.005 ccode_471_time -0.0106 8.85e-05 -119.747 0.000 -0.011 -0.010 ccode_475_time 0.0002 0.000 1.308 0.191 -0.000 0.001 ccode_481_time -0.0005 0.000 -2.518 0.012 -0.001 -0.000 ccode_482_time 4.412e-05 4.9e-05 0.901 0.368 -5.19e-05 0.000 ccode_483_time -0.0185 0.001 -31.860 0.000 -0.020 -0.017 ccode_484_time 0.0415 0.000 162.672 0.000 0.041 0.042 ccode_490_time -0.0379 0.000 -88.293 0.000 -0.039 -0.037 ccode_500_time -0.0095 0.000 -25.149 0.000 -0.010 -0.009 ccode_501_time -0.0133 0.000 -45.306 0.000 -0.014 -0.013 ccode_510_time -3.845e-05 2.79e-05 -1.376 0.169 -9.32e-05 1.63e-05 ccode_516_time 0.0662 0.000 249.253 0.000 0.066 0.067 ccode_517_time 0.0697 0.000 244.485 0.000 0.069 0.070 ccode_520_time -0.0015 0.002 -0.705 0.481 -0.006 0.003 ccode_522_time 0.0437 0.001 30.559 0.000 0.041 0.047 ccode_530_time -0.0246 6.17e-05 -398.471 0.000 -0.025 -0.024 ccode_540_time -0.0004 0.000 -2.421 0.015 -0.001 -6.67e-05 ccode_541_time -0.0737 0.000 -337.720 0.000 -0.074 -0.073 ccode_551_time 5.597e-05 5.52e-05 1.014 0.310 -5.22e-05 0.000 ccode_552_time 0.0301 0.000 159.587 0.000 0.030 0.030 ccode_553_time 7.894e-05 0.000 0.257 0.797 -0.001 0.001 ccode_560_time -0.0684 5.73e-05 -1192.513 0.000 -0.068 -0.068 ccode_565_time 0.1134 0.001 80.950 0.000 0.111 0.116 ccode_570_time 0.0142 0.000 100.113 0.000 0.014 0.014 ccode_571_time 0.0002 0.000 1.506 0.132 -6.68e-05 0.001 ccode_572_time -0.0001 5.78e-05 -2.521 0.012 -0.000 -3.24e-05 ccode_580_time -0.0002 0.000 -1.087 0.277 -0.001 0.000 ccode_625_time 0.0331 3.67e-05 901.316 0.000 0.033 0.033 ============================================================================== Omnibus: 91.156 Durbin-Watson: 1.478 Prob(Omnibus): 0.000 Jarque-Bera (JB): 421.027 Skew: 0.452 Prob(JB): 3.76e-92 Kurtosis: 6.575 Cond. No. 226. ============================================================================== Notes: [1] Standard Errors are robust to cluster correlation (cluster) 
smf.ols(formula_model1, data=data).fit(cov_type = 'HC1').summary2().tables[1]

model1 = smf.ols(formula_model1, data=data).fit(cov_type = 'HC1').summary2().tables[1]
model1_coef = model1.iloc[1,0]  # fila posición 1 y columan posición 0
model1_coef_se = model1.iloc[1,1] # fila posición 1 y columan posición 1

# HC1: standar error robust aginst heterocedasticity

model1_lower = model1.iloc[1,4]
model1_upper = model1.iloc[1,5]

Modelo 2

formula_model2 = "war_prio ~ GPCP_g + GPCP_g_l + C(ccode)" + ' + ' + ' + '.join( country_trend )

ols_model2 = smf.ols(formula_model1, data=data).fit(cov_type='cluster', cov_kwds={'groups': data['ccode']})

print(ols_model2.summary())
OLS Regression Results ============================================================================== Dep. Variable: any_prio R-squared: 0.708 Model: OLS Adj. R-squared: 0.672 Method: Least Squares F-statistic: 162.4 Date: Sat, 10 Dec 2022 Prob (F-statistic): 6.28e-20 Time: 21:15:19 Log-Likelihood: 8.6565 No. Observations: 743 AIC: 150.7 Df Residuals: 659 BIC: 538.0 Df Model: 83 Covariance Type: cluster ===================================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------------- Intercept -0.2458 0.004 -66.965 0.000 -0.253 -0.239 C(ccode)[T.420.0] 0.4921 0.001 468.956 0.000 0.490 0.494 C(ccode)[T.432.0] 0.2600 0.008 32.651 0.000 0.244 0.276 C(ccode)[T.433.0] -0.0986 0.002 -59.007 0.000 -0.102 -0.095 C(ccode)[T.434.0] 0.2438 0.004 57.888 0.000 0.236 0.252 C(ccode)[T.435.0] 0.2377 0.009 26.715 0.000 0.220 0.255 C(ccode)[T.436.0] 0.1243 0.013 9.406 0.000 0.098 0.150 C(ccode)[T.437.0] 0.2450 0.004 61.386 0.000 0.237 0.253 C(ccode)[T.438.0] -0.0061 0.003 -2.184 0.029 -0.012 -0.001 C(ccode)[T.439.0] 0.4443 0.004 105.272 0.000 0.436 0.453 C(ccode)[T.450.0] -0.3559 0.004 -92.197 0.000 -0.363 -0.348 C(ccode)[T.451.0] -0.2257 0.002 -120.766 0.000 -0.229 -0.222 C(ccode)[T.452.0] 0.6882 0.004 159.022 0.000 0.680 0.697 C(ccode)[T.461.0] 0.4129 0.005 88.451 0.000 0.404 0.422 C(ccode)[T.471.0] 0.4257 0.004 106.398 0.000 0.418 0.433 C(ccode)[T.475.0] 0.2433 0.005 44.966 0.000 0.233 0.254 C(ccode)[T.481.0] 0.2542 0.002 162.114 0.000 0.251 0.257 C(ccode)[T.482.0] 0.2466 0.003 72.125 0.000 0.240 0.253 C(ccode)[T.483.0] 1.3664 0.008 162.199 0.000 1.350 1.383 C(ccode)[T.484.0] -0.0916 0.002 -58.163 0.000 -0.095 -0.088 C(ccode)[T.490.0] 1.3481 0.001 1581.192 0.000 1.346 1.350 C(ccode)[T.500.0] 1.2554 0.002 743.377 0.000 1.252 1.259 C(ccode)[T.501.0] 0.4613 0.005 92.925 0.000 0.452 0.471 C(ccode)[T.510.0] 0.2446 0.004 60.659 0.000 0.237 0.252 C(ccode)[T.516.0] -0.1279 0.001 -184.968 0.000 -0.129 -0.127 C(ccode)[T.517.0] -0.1171 0.001 -198.672 0.000 -0.118 -0.116 C(ccode)[T.520.0] 1.2725 0.021 59.568 0.000 1.231 1.314 C(ccode)[T.522.0] -0.0132 0.008 -1.660 0.097 -0.029 0.002 C(ccode)[T.530.0] 1.3320 0.002 620.380 0.000 1.328 1.336 C(ccode)[T.540.0] 1.2494 0.002 611.501 0.000 1.245 1.253 C(ccode)[T.541.0] 1.7636 0.004 405.215 0.000 1.755 1.772 C(ccode)[T.551.0] 0.2440 0.005 53.114 0.000 0.235 0.253 C(ccode)[T.552.0] -0.0049 0.003 -1.585 0.113 -0.011 0.001 C(ccode)[T.553.0] 0.2460 0.005 44.830 0.000 0.235 0.257 C(ccode)[T.560.0] 1.7528 0.002 796.854 0.000 1.749 1.757 C(ccode)[T.565.0] -1.4419 0.030 -47.517 0.000 -1.501 -1.382 C(ccode)[T.570.0] 0.1329 0.002 74.355 0.000 0.129 0.136 C(ccode)[T.571.0] 0.2493 0.002 110.308 0.000 0.245 0.254 C(ccode)[T.572.0] 0.2519 0.001 329.982 0.000 0.250 0.253 C(ccode)[T.580.0] 0.2488 0.002 147.237 0.000 0.245 0.252 C(ccode)[T.625.0] 0.7557 0.003 246.088 0.000 0.750 0.762 GPCP_g -0.0238 0.043 -0.550 0.582 -0.108 0.061 GPCP_g_l -0.1219 0.052 -2.352 0.019 -0.224 -0.020 ccode_404_time 0.0295 0.000 185.142 0.000 0.029 0.030 ccode_420_time -0.0160 0.000 -143.094 0.000 -0.016 -0.016 ccode_432_time 0.0078 0.001 15.618 0.000 0.007 0.009 ccode_433_time 0.0595 5.87e-05 1014.282 0.000 0.059 0.060 ccode_434_time 0.0004 0.000 2.272 0.023 4.97e-05 0.001 ccode_435_time 0.0009 0.001 1.469 0.142 -0.000 0.002 ccode_436_time 0.0369 0.001 34.562 0.000 0.035 0.039 ccode_437_time -1.254e-05 5.51e-06 -2.276 0.023 -2.33e-05 -1.74e-06 ccode_438_time 0.0297 0.000 270.544 0.000 0.030 0.030 ccode_439_time -0.0032 0.000 -19.303 0.000 -0.004 -0.003 ccode_450_time 0.1090 0.000 656.335 0.000 0.109 0.109 ccode_451_time 0.0786 0.000 259.266 0.000 0.078 0.079 ccode_452_time -0.0281 0.000 -211.081 0.000 -0.028 -0.028 ccode_461_time -0.0052 0.000 -42.688 0.000 -0.005 -0.005 ccode_471_time -0.0106 8.85e-05 -119.747 0.000 -0.011 -0.010 ccode_475_time 0.0002 0.000 1.308 0.191 -0.000 0.001 ccode_481_time -0.0005 0.000 -2.518 0.012 -0.001 -0.000 ccode_482_time 4.412e-05 4.9e-05 0.901 0.368 -5.19e-05 0.000 ccode_483_time -0.0185 0.001 -31.860 0.000 -0.020 -0.017 ccode_484_time 0.0415 0.000 162.672 0.000 0.041 0.042 ccode_490_time -0.0379 0.000 -88.293 0.000 -0.039 -0.037 ccode_500_time -0.0095 0.000 -25.149 0.000 -0.010 -0.009 ccode_501_time -0.0133 0.000 -45.306 0.000 -0.014 -0.013 ccode_510_time -3.845e-05 2.79e-05 -1.376 0.169 -9.32e-05 1.63e-05 ccode_516_time 0.0662 0.000 249.253 0.000 0.066 0.067 ccode_517_time 0.0697 0.000 244.485 0.000 0.069 0.070 ccode_520_time -0.0015 0.002 -0.705 0.481 -0.006 0.003 ccode_522_time 0.0437 0.001 30.559 0.000 0.041 0.047 ccode_530_time -0.0246 6.17e-05 -398.471 0.000 -0.025 -0.024 ccode_540_time -0.0004 0.000 -2.421 0.015 -0.001 -6.67e-05 ccode_541_time -0.0737 0.000 -337.720 0.000 -0.074 -0.073 ccode_551_time 5.597e-05 5.52e-05 1.014 0.310 -5.22e-05 0.000 ccode_552_time 0.0301 0.000 159.587 0.000 0.030 0.030 ccode_553_time 7.894e-05 0.000 0.257 0.797 -0.001 0.001 ccode_560_time -0.0684 5.73e-05 -1192.513 0.000 -0.068 -0.068 ccode_565_time 0.1134 0.001 80.950 0.000 0.111 0.116 ccode_570_time 0.0142 0.000 100.113 0.000 0.014 0.014 ccode_571_time 0.0002 0.000 1.506 0.132 -6.68e-05 0.001 ccode_572_time -0.0001 5.78e-05 -2.521 0.012 -0.000 -3.24e-05 ccode_580_time -0.0002 0.000 -1.087 0.277 -0.001 0.000 ccode_625_time 0.0331 3.67e-05 901.316 0.000 0.033 0.033 ============================================================================== Omnibus: 91.156 Durbin-Watson: 1.478 Prob(Omnibus): 0.000 Jarque-Bera (JB): 421.027 Skew: 0.452 Prob(JB): 3.76e-92 Kurtosis: 6.575 Cond. No. 226. ============================================================================== Notes: [1] Standard Errors are robust to cluster correlation (cluster) 
model2 = smf.ols(formula_model2, data=data).fit(cov_type = 'HC1').summary2().tables[1]
model2_coef = model2.iloc[1,0]  # fila posición 1 y columan posición 0
model2_coef_se = model2.iloc[1,1] # fila posición 1 y columan posición 1

# HC1: standar error robust aginst heterocedasticity

model2_lower = model2.iloc[1,4]
model2_upper = model2.iloc[1,5]

table = np.zeros( ( 2, 4 ) )

table[0,0] = model1_coef
table[0,1] = model1_coef_se 
table[0,2] = model1_lower
table[0,3] = model1_upper 

table[1,0] = model2_coef
table[1,1] = model2_coef_se  
table[1,2] = model2_lower
table[1,3] = model2_upper 
table_pandas = pd.DataFrame( table, columns = [ "Estimate","Std. Error","Lower_bound" , "Upper_bound"])
table_pandas.index = [ "OLS any_prior","OLS war_prior",]

table_pandas.reset_index(inplace = True)
table_pandas.rename(columns = {"index" : "Model"}, inplace = True)

table_pandas.round(8)

fig, ax = plt.subplots(figsize=(7, 6))

ax.scatter(x=table_pandas['Model'], 
         marker='o', s=20,  # s: modificar tamaño del point
         y=table_pandas['Estimate'], color = "red")

eb1 = plt.errorbar(x=table_pandas['Model'], y=table_pandas['Estimate'],
            yerr = 0.9*(table_pandas['Upper_bound']- table_pandas['Lower_bound']),
            color = 'black', ls='', capsize = 4)

# ls='': no une los puntos rojos 
#  yerr genera el gráfico del intervalo de confianza 

plt.axhline(y=0, color = 'black').set_linestyle('--')  # linea horizontal 
# Set title & labels
plt.title('Coef Plox (95% CI)',fontsize=12)
Text(0.5, 1.0, 'Coef Plox (95% CI)')
Image in a Jupyter notebook

Pregunta 3

#Primero instalamos y cargamos las librerías que se van a necesitar
!pip install pandas
!pip install matplotlib
!pip install geopandas
!pip install contextily
!pip install numpy
#MatPlotlib inline
import pandas as pd
import numpy as np
import contextily as cx
import matplotlib.pyplot as plt 
import geopandas as gpd 


#Cargamos el directorio primero

Dist_Mita = gpd.read_file(r'C:\Users\Alexander\Documents\GitHub\1ECO35_2022_2\data\Mita\mita.gdb', layer=5)

#Hay que cargar los shapefiles

Pot1 = r'C:\Users\Alexander\Documents\GitHub\1ECO35_2022_2\Trabajo_final\datos\Mita\pot_line.shp'
Pot2 = r'C:\Users\Alexander\Documents\GitHub\1ECO35_2022_2\Trabajo_final\datos\Mita\huan_line.shp'
Pot3 = r'C:\Users\Alexander\Documents\GitHub\1ECO35_2022_2\Trabajo_final\datos\Mita\MitaBoundary.shp'

#Creamos los sistemas de coordenadas

varPot1 = gpd.read_file(Pot1)
varPot2 = gpd.read_file(Pot2)
varPot3 = gpd.read_file(Pot3)

#Estandarizamos los sistemas de coordenadas

varPot1 = varPot1.to_crs('EPSG:4326')
varPot2 = varPot2.to_crs('EPSG:4326')
varPot3 = varPot3.to_crs('EPSG:4326')

#Concatenamos dos de los shapefiles para poder visualizarlos en un solo gráfico

Var0 = gpd.pd.concat([varPot1, varPot2])

#Creamos y corremos el mapa, sin los ejes y con la leyenda respectiva

ax = Var0.plot(color = 'black')

#Planteamos las dimensiones del gráfico \n",
   f, ax = plt.subplots(figsize=(12,12)) \n,
    \n,
    prueba1['geometry'].plot(color='blue', edgecolor='black', zorder=0.5, ax = ax) \n,
    #Introducimos la primera línea en nuestro mapa de color negro  \n,
    prueba2['geometry'].plot(color = 'yellow', edgecolor='gold', zorder=0.5, ax = ax, label=\"Study Boundary\")    \n,
    #Introducimos la segunda línea en nuestro mapa de color gris con borde blanco y le añadimos la etiqueta \"Study Boundary\"\n",
    prueba3['geometry'].plot(color = 'blue', edgecolor='black', zorder=0.5, ax = ax, label=\"Mita Boundary\")\n,
    #Introducimos la tercera línea en nuestro mapa de color negro y le añadimos la etiqueta \"Mita Boundary\". \n",
    #Solo ponemos esta e intepretaremos el color como indicador de prueba 1 y prueba 3 \n",
    \n,
    # Borramos los valores de los ejes\n",
    \n,
    plt.xticks([])\n,
    plt.yticks([])\n,
    \n,
    #Añadimos los nombres siguiendo las cooredenadas encontradas con prueba y error. \n",
    \n,
    # Añadimos Huancavelica \n,
    f.text(0.21,0.72,'Huancavelica',color = 'black', size = 11,\n,
            bbox=dict(facecolor='none', edgecolor='none', pad=6.0)) \n,
    \n,
    # Añadimos Potosí \n",
    f.text(0.76,0.30,'Potosí',color = 'black', size = 11,\n,
            bbox=dict(facecolor='none', edgecolor='none', pad=6.0)) \n,
    
    \n,
   # Añadimos Uyuni Salt Flat\n",
   f.text(0.62,0.24,'Uyuni Salt Flat',color = 'black', size = 11,\n,
           bbox=dict(facecolor='none', edgecolor='none', pad=6.0)) \n,
   \n,
   # Añadimos un mapa de fondo que no se parece mucho al original. Pero, de cualquier forma, nos sirve para darle algo de color\n",
   # al trabajo. El original se creó con altitudos y por este motivo no podemos generarlo. \n",
   \n,
   cx.add_basemap(ax, crs=\"EPSG:4326\", attribution = False)\n,
   \n,
   \n,
   # Finalmente, agregamos la leyenda que considera las etiquetas previamente puestas en el perimetro de la mita y de estudio.\n",
   \n,
   plt.legend(loc='upper left',\n,
              title = \"\",frameon=True,\n,
               bbox_to_anchor=(0, 0.15), prop={'size': 12})\n,
   plt.savefig(\"mapa2.png\")
  ]
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