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I am doing some Maximum Likelihood Estimation with a density that has time-varying parameters. I am using the fmincon function in Matlab, but I do not know how to impose restrictions on a vector of parameters. To be clear, I have a vector of parameters (that do not change over time) and I know how to impose restrictions on them. However I want to make two parameters time-varying and I do not know how to impose restrictions on vectors containing those time-varying parameters. Summing up, I have a vector of a few time-invariant parameters and two vectors of time-varying parameters.

I found a paper that suggests using logistic mapping of the form $ \Theta_{t, restricted} = L + \frac{(U-L)}{1+exp(-\Theta_t)}$ where $\Theta_t = (x_1, x_2)$ and U and L are the upper and lower bounds respectively, for the restricted parameter. $x_1, x_2$ are the time-varying parameters on which I want to impose restrictions.

I would be grateful for any help regarding implementing this logistic mapping into my optimization problem.

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Here is an MLE I built that uses logistic mapping.

%MLE iterator:
for cxm = 1:cxmax
    for cxth = 1:wx; %thx 
        %Incr. theta within asymptotic min and max.
            thi1 = thA1(cxth,1); mint = thA1(cxth,2); maxt = thA1(cxth,3);
            thix = -log((maxt - mint)/(thi1 - mint) - 1); %Logistic inverse.
            if rand > 0.5; signx = -1; else signx = 1; end
            expn = thix + 0.25 * signx / cxm; 
            thi2 = mint + ((maxt - mint)/(1 + exp(-expn))); %Logistic.
         %Calc. change in log likelihood.
            thA2 = thA1; 
            thA2(cxth,1) = thi2;
            %Constraint(s):
                thA2(wx+1:wx*2,1) = min(1,thA2(wx+1:wx*2,1) / sum(thA2(wx+1:wx*2,1))); 
            [llk1] = llkF(rC,thA1,tx,wx);
            [llk2] = llkF(rC,thA2,tx,wx);
        %Calc. update.
            thA3 = thA1;
            expn = thix + 1 * ((llk2 - llk1) / (expn - thix)) / cxm; 
            thA3(cxth,1) = mint + ((maxt - mint)/(1 + exp(-expn))); %Logistic.
            %Constraint(s):
                thA3(wx+1:wx*2,1) = min(1,thA3(wx+1:wx*2,1) / sum(thA3(wx+1:wx*2,1))); 
            [llk3] = llkF(rC,thA3,tx,wx);
        %Update thA1 only if thA2 or thA3 is better.
            disp([llk1 llk2 llk3]); %<<<<<<<<<<<<<<<<<<<<<<
            if llk2 > llk1 && llk2 > llk3; thA1 = thA2; llk1 = llk2; end
            if llk3 > llk1 && llk3 > llk2; thA1 = thA3; llk1 = llk3; end
    end%theta loop.
    llkV(cxm) = llk1;
    %disp(['     MLE: ', num2str(cxm),', ll = ',num2str(llk1)])
    %Test for MLE convergence.
        if cxm > 5;
            d5llk = (llkV(cxm) - llkV(cxm - 5)); %Calc. change in llk, lag 5.
            if d5llk < 0.0001; llkV(cxm + 1:cxmax) = llkV(1); 
                break; end; 
        end%test
end%MLE loop.
llkV = llkV(1:cxm); %Truncate away the zeros.
thA = thA1; %Reset theta array.
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