andreasnoack
commented
1 year ago Please provide some more context for this problem and please also make the example self contained such that others can run it. Otherwise there is little chance that you'd receive any helpful replies here.
age=sh["F2:F3778"] sex=sh["D2:D3778"] wage=sh["I2:I3778"] work=sh["H2:H3778"] family_size=sh["C2:C3778"] data = (;age,sex,wage,work,family_size); X=[ones(n) age sex family_size work] beta = [:beta1,:beta2,:beta3,:beta4,:beta5,:beta6 ] beta_ml=zeros(6,1) xf = XLSX.readxlsx("HW1.xlsx") n=3777 sh = xf["Sheet1"] age=sh["F2:F3778"] sex=sh["D2:D3778"] wage=sh["I2:I3778"] work=sh["H2:H3778"] family_size=sh["C2:C3778"] data = (;age,sex,wage,work,family_size); X=[ones(n) age sex family_size work] beta = [:beta1,:beta2,:beta3,:beta4,:beta5,:beta6 ] beta_ml=zeros(6,1) result=optimize(beta->sum(.-log(1/sqrt(2πbeta[6]^2)) .+ 1/(2 beta[6]^2) . ((wage.-beta[5](beta[1]+beta[2]age+beta[3]sex+beta[4]family_size).work.^((beta[1]+beta[2]age+beta[3]sex+beta[4]family_size).-1))./(beta[5])(1 .+(beta[1]+beta[2]age+beta[3]sex+beta[4]family_size).log(work)).(work.^(beta[1]+beta[2]age+beta[3]sex+beta[4]*family_size).-1)).^2),[0.0,0.0,0.0,0.0,0.0,0.0]) beta_ml=Optim.minimizer(result)