Grupo_5_pynb.ipynb

Kernel: Julia 1.6.7

#Ejercicio 5: Traducción de OLS de phyton a julia
using Random
using Statistics 
using Distributions, LinearAlgebra
using DataFrames
Random.seed!(756)

x1 = rand(500)  # uniform distribution
x2 = rand(500) # uniform distribution
x3 = rand(500) # uniform distribution
x4 = rand(500) # uniform distribution
e = randn(500) # normal dsitribution mean = 0 y sd = 1
z = rand(500)

# DGP

Y = ones(500) + 0.8.*x1 + 1.2.*x2 + 0.5.*x3 + 1.5.*x4 + e

X = hcat(ones(500),x1,x2,x3,x4)

function ols(M::Matrix , est::Bool = true, pvalue::Bool = true, instrumento = nothing, index = nothing)
    if est && pvalue && isnothing(instrumento) && isnothing(index) # V y V: V 
        
        beta = inv(transpose(X)*X)*(transpose(X)*Y)
        y_est =  X * beta 
        n = size(X,1)     #número de observaciones contando filas (500)
        k = size(X,2) - 1 #número de variables menos el intercepto (columnas - 1) (4)
        nk = n - k #grados de libertad
        sigma =  (sum(string( map((x) -> x^2, Y - y_est)   ))) / nk 
        Var = sigma*inv(transpose(X)*X)
        sd = Var[ diagind(Var) ].^2
        t_est = abs(beta/sd)
        dist = TDist(4)  # 4 grados de libertad
        pvalue = 2*(1 - cdf(dist, test))
        df = DataFrame( OLS = beta , standar_error = sd , Pvalue = pvalue)
        
    elseif !isnothing(instrumento) && !isnothing(index)
        
        beta = inv(transpose(X)*X)*(transpose(X)*Y)
        
        index = index  - 1 
        Z = X
        Z[:,index] = z
        beta_x = inv(transpose(Z)*Z)*(transpose(Z)*X[:,index] ) 
        x_est  = Z * beta_x
        X[:,index] = x_est
        beta_iv = inv(transpose(X)*X)*(transpose(X)*Y)
        df = Dataframe( OLS = beta , OLS_IV = beta_iv)
    end
end
    return df



ols(X,Y)

ols(X,Y,instrumento = z, index = 1)

UndefVarError: df not defined

Stacktrace:
 [1] top-level scope
   @ In[46]:51
 [2] eval
   @ .\boot.jl:360 [inlined]
 [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
   @ Base .\loading.jl:1116

#Comprobación línea por línea
using Random
using Statistics 
using Distributions, LinearAlgebra
using DataFrames
Random.seed!(756)

x1 = rand(500)  # uniform distribution
x2 = rand(500) # uniform distribution
x3 = rand(500) # uniform distribution
x4 = rand(500) # uniform distribution
e = randn(500) # normal dsitribution mean = 0 y sd = 1
z = rand(500)

# DGP

Y = ones(500) + 0.8.*x1 + 1.2.*x2 + 0.5.*x3 + 1.5.*x4 + e

X = hcat(ones(500),x1,x2,x3,x4)

function ols(M::Matrix , est::Bool = true, pvalue::Bool = true, instrumento = nothing, index = nothing)
    if est && pvalue && isnothing(instrumento) && isnothing(index) # V y V: V 

     beta = inv(transpose(X)*X)*(transpose(X)*Y)
     y_est =  X * beta 
     n = size(X,1)
     k = size(X,2) - 1
     nk = n - k
     sigma =sum(any(map((x) -> x.^2,Y-y_est))) / nk 

    end
end
return sigma

#Tuvimos problemas para traducir el sigma y lo demás se tradujo. Pero, al no poder pasar del código de sigma no podemos confir-
#mar si la traducción es correcta

        

500-element Vector{Float64}:
 5.4031216073460805e-5
 0.0022181722627542654
 0.000790960259504303
 0.0007112961665073163
 0.00032634339912251015
 0.004831753130268498
 0.005019252688530333
 0.000288060620731548
 7.174040487996433e-5
 0.00011049075694795605
 0.0004795361534546874
 0.00023878930841659334
 0.00019166141127916798
 ⋮
 0.0004397426700596576
 0.006165252292643008
 0.0018333857917609967
 0.001544754247826793
 0.00011580608403475287
 0.010760224300972383
 0.0009984917503637041
 7.835101346891622e-5
 0.0015877328914698987
 0.0009349801824308511
 0.0003167844872841053
 3.4382481100425256e-5