Grupo_8_jl.ipynb

Kernel: Julia 1.8.0

#Ejercicio 5
using Random
using Distributions
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

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, Y, standar = true, Pvalue = true, instrumento = nothing, index = nothing)

     if standar && Pvalue && isnothing(instrumento) && isnothing(index)
        
        beta = inv(transpose(X) * X) * (transpose(X) * 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*inv(transpose(X) * X)
        sd = np.sqrt( np.diag(Var) )
        t_est = np.absolute(beta/sd)
        test = 3.5
        dist = TDist(4)  # 4 grados de libertad
        Pvalue = 2*(1 - cdf(dist, test))
        df = pd.DataFrame( {"OLS": beta , "standar_error" : sd ,
                             "Pvalue" : pvalue} )
        
    elseif !isnothing(index) && !isnothing(instrumento)
        
        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 = pd.DataFrame( {"OLS": beta , "OLS_IV" : beta_iv})
        
    end
    
ols(X,Y)

ols(X,Y,instrumento = z, index = 2)
    
    

syntax: missing comma or ) in argument list

Stacktrace:
 [1] top-level scope
   @ In[68]:27
 [2] eval
   @ .\boot.jl:368 [inlined]
 [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
   @ Base .\loading.jl:1428