What is the equivalent Julia code for statsmodels.api.OLS

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It’s here: Home · GLM

coef(reg)[2] / stderror(reg)[2]

The only thing is the range of output. It seems like in Python, the values are multiplied by a constant value > 1 (it seems that the final BSADF values in Julia should be multiplied by 10):

This I do not understand. However, I took the OLS equations from the top of my head, and they work fine in a toy example:

using LinearAlgebra, GLM
X = [
    1 3 4
    2 8 1
    4 5 2
    6 7 2
    9 4 3
]

y = [5,4,6,2,3]
Xc = hcat(ones(size(X,1)), X)

# GLM:

regglm = glm(Xc, y, Normal(), IdentityLink())

# easy fit

reglm = fit(LinearModel, Xc, y)

#manual
cvmat = inv(Xc' * Xc)
cf = cvmat * Xc' * y
resid = norm(Xc * cf .- y)
se = sqrt.(diag(cvmat)) * resid


println("GLM:\n", regglm)
println("\n\nLM:\n", reglm)
println("\n\nMAN:")
[cf se]

and the same in python:

import numpy as np
import statsmodels.api as sm

X = np.array([[1, 3, 4],[2,8,1], [4,5,2], [6,7,2], [9,4,3]])
y = np.array([5,4,6,2,3])
reg = sm.OLS(y,sm.add_constant(X))
res = reg.fit()
res.params
res.bse

They are all equal.

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