I need some tips on estimating panel data using nonlinear switching regression technique. AFAIK there are two ways of solving it. The model I estimate is the following: panel Price const Cost logist Domestic_Sales Export_Sales Soda PCFT Total_import Logistic function is given as logist = 1/(1+exp(1,81*(mu-HHI)/sig)). Height of the logistic function is between 0.025 and 0.975. Mu = mean(height) Sig = var(height) The datafile is attached in case anybody wants to help tackle the model directly. The theoretic foundation for this analysis is also attached in the PDF article. I tried to use simultaneous equations to estimate this model properly but to no avail. Second best option is to use loop constructs to model all possible parameter values in the given range then to choose the model with the highest $rsq. Mu should be in the range of 0.28 to 0.501 and sig between 0.01 and 0.201. However I have a problem with implementing a proper loop. Here is an example nulldata 100 set seed 2478 HHI =randgen(u,0.21,0.27) P = randgen(u,100,300) # open two loops for estimated params loop for (mu=0.28; mu<0.501; mu+=.01) loop for (sig=0.01; sig<0.201; sig+=.01) # construct the dependent variables genr height = randgen(u,0.025,0.975) genr mu = mean(height) genr sig = var(height) genr logist = 1/(1+exp((mu-HHI)/sig)) # run OLS regression as an example ols P const logist # grab the coefficient estimates and R-squared genr b1 = $coeff(const) genr b2 = $coeff(logist) genr r2 = $rsq print a b r2 store coeffs.gdt a b r2 endloop endloop

genr mu = mean(height) genr sig = var(height)