Grupo_7_py.ipynb

Kernel: Python 3 (ipykernel)

Tarea 1

Pregunta 1

import math

for i in range (100):

    if i>0 and i<=100 :
    
        i = i ^ (1/2) 
    
    elif i>100 and i<=300 :
    
        i = i-5

    else :
        
        i = 50
        
print(i)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_13976\3977639358.py in <module>
      3     if i>0 and i<=100 :
      4 
----> 5         i = i ^ (1/2)
      6 
      7     elif i>100 and i<=300 :
TypeError: unsupported operand type(s) for ^: 'int' and 'float'

Pregunta 2

import numpy as np
import random
import math

y = np.arrange(0,100)

def calculator(y,h,z):
    
    h = max(y)
    z = min(y)
    
    if not isinstance( y , np.arange ) :      
        raise TypeError( "x must be a n-arrange")
        
    result = (y - h) / (z - h)
    
    return result

calculator(y, h, z)
print( calculator( y, h, z )
      

M = np.array(-10, 0, 500)

def calculator(M,k,l):
    
    if not isinstance( M , np.narray ) :      
        raise TypeError( "x must be a n-array")
    k = max(M)
    l = max(M)
    result = (M - k) / (l - k)
    
    return result

calculator(M, k, l)
print( calculator( M, k, l )

  File "C:\Users\Jose Pastor\AppData\Local\Temp\ipykernel_13976\1854637588.py", line 23
    M = np.array(-10, 0, 500)
    ^
SyntaxError: invalid syntax

Pregunta 3

import numpy as np
import random
import pandas as pd

pd.set_option('display.float_format', lambda x: '%.4f' % x)   # seteando a 4 decimales 

np.random.seed(175)

df = pd.DataFrame({'Tamaño de muestra': [0],
                   'Regresor': [0],
                   'Coeficiente': [0],
                   'Error estándar': [0]})

muestras = [10, 50, 80, 120, 200, 500, 800, 1000, 5000]

for i in muestras:
    
    x1 = np.array(random.sample(range(10000), i))  # números aleatorios unicos en el rango [0, 10000]
    x2 = np.array(random.sample(range(10000), i))  
    x3 = np.array(random.sample(range(10000), i))  
    x4 = np.array(random.sample(range(10000), i))  
    x5 = np.array(random.sample(range(10000), i))
    e  = np.random.normal(0, 1, i)                 # normal distribution mean = 0 and sd = 1

    # Poblacional regression (Data Generating Process GDP)
    Y = 1 + 0.2*x1 + 0.4*x2 + 0.6*x3 + 0.8*x4 + e

    X = np.column_stack((np.ones(i), x1, x2, x3, x4))   # añadiendo matriz de unos

    beta = np.linalg.inv(X.T @ X) @ ((X.T) @ Y )      
    beta

    lista_sd = 0, np.std(x1, ddof=1), np.std(x2, ddof=1), np.std(x3, ddof=1), np.std(x4, ddof=1)
    lista_sd
    
    lista_regresores = ['x1', 'x2', 'x3', 'x4', 'x5']

    lista_muestra_10 = np.full((5, 1), i).tolist()
    lista_muestra_50 = np.full((5, 1), i).tolist()
    lista_muestra_80 = np.full((5, 1), i).tolist()
    lista_muestra_120 = np.full((5, 1), i).tolist()
    lista_muestra_200 = np.full((5, 1), i).tolist()
    lista_muestra_500 = np.full((5, 1), i).tolist()
    lista_muestra_800 = np.full((5, 1), i).tolist()
    lista_muestra_1000 = np.full((5, 1), i).tolist()
    lista_muestra_5000 = np.full((5, 1), i).tolist()
    
    
    df_muestra = pd.DataFrame({'Tamaño de muestra': lista_muestra_10,
                             'Regresor': lista_regresores,
                             'Coeficiente': beta,
                             'Error estándar': lista_sd})
    df = pd.concat([df, df_muestra], axis=0).reset_index(drop=True)   
    
df = df.drop(df.index[:1])  # elimina la primera fila
df

# Comentario:
# Se puede observar que a medida que el tamaño de muestra aumenta, los coeficientes estimados se acercan cada vez más a los coeficientes originales.

Pregunta 4

import random
import numpy as np
import pandas as pd
from scipy.stats import t # t - student 
import matplotlib.pyplot as plt  

random.seed(175)
x1=np.random.rand(800)
x2=np.random.rand(800)
x3=np.random.rand(800)
x4=np.random.rand(800)
x5=np.random.rand(800)
x6=np.random.rand(800)
x7=np.random.rand(800)
c=1
X = np.array ([x1,x2,x3,x4,x5,x6,x7])
beta = np.array([c, 0.8, 1.2, 0.5, 1.5, 0.8, 1.2])
e = np.random.normal(0,1,800)
print(np.size(e))
print ("hubh ", e)

z = np.random.rand(800)

Y = c + 0.8*x1 + 1.2*x2 + 0.5*x3 + 1.5*x4 + 0.8*x5 + 1.2*x6 + 1.5*x7 + e
print ("valorY ", np.size(Y))

def ols(M,Y, standar = True, Pvalue = True , instrumento = None, index = None):

    if standar and Pvalue and (instrumento is None)  and (index is None) :
        
         beta = np.linalg.inv(X.T @ X) @ ((X.T) @ Y ) 
         y_est =  X @ beta 
         n = X.shape[0]
         k = X.shape[1] - 1 
         nk = n - k    
         sigma =  sum(list( map( lambda x: x**2 , Y - y_est)   )) / nk 
         Var = sigma*np.linalg.inv(X.T @ X)
         sd = np.sqrt( np.diag(Var) )
         t_est = np.absolute(beta/sd)
         pvalue = (1 - t.cdf(t_est, df=nk) ) * 2
         df = pd.DataFrame( {"OLS": beta , "standar_error" : sd ,
                             "Pvalue" : pvalue})   

     

800
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valorY  800