* Nonlinear Regression for Cobb-Douglas Production Function * * Keywords: * regression, nonlinear, r-squared, autocorrelation, dw, durbin-watson, * test, cobb-douglas, production, funciton, mexico * * Description: * We illustrate how to estimate a Nonlinear Exponential Model for Mexican * Cobb-Douglas Production Function, calculate R-Squared for this model and * conduct Durbin-Watson Autocorrelation Test * * Author(s): * Skif Pankov * * Source: * Damodar N. Gujarati and Dawn C. Porter, Basic Econometrics - 5th Edition * McGraw-Hill International Edition, Chapter 14, Example 14.2 (page 532) * sample 1 20 * Reading the datafile and naming variables read (data_14.2.shd) y x2 x3 * Initiating nonlinear regression procedure, stating that 1 equation with * 3 coefficients should be estimated and storing the estimated derivatives, * coefficients, residuals and predicted values nl 1 / ncoef = 3 zmatrix = z coef = beta predict = yhat resid = reds * Writing up an equation we would like to estimate - coefficients are named * A, B and C eq y = A*(x2**B)*(x3**C) * Stating the initial values of coefficients - these shall be used as the starting * points in the process of iterative estimation coef A 0.5 B 0.5 C 0.5 end * Generating R^2 gen1 su2 = sum(reds**2) gen1 sy = sum((y-sum(y)/\$N)**2) gen1 r2 = 1 - (su2/sy) print r2 * Generating a linear pseudomodel and computing the Durbin-Watson p-value matrix ybar = y - yhat + z*beta ols ybar z / noconstant dwpvalue stop