> Dear all, > > For estimating Cobb-Douglad production Function [ Y = ALPHA * > (L^(BETA1)) * > (K^(BETA2)) ], i want to use nls function (without > linearizing it). But > how can i get initial values? > > ------------------------------------ > > options(prompt=" R> " ) > R> Y <- c(59.6, 63.9, 73.5, 75.6, 77.3, 82.8, 83.6, 84.9, > 90.3, 80.5, > 73.5, 60.3, 58.2, 64.4, 75.4, 85, 92.7, 85.4, 92.3, 101.2, > 113.3, 107.8, > 105.2, 107.1, 108.8, 131.4, 130.9, 134.7, 129.1, 147.8, 152.1, 154.3, > 159.9) # production > R> L <- c(39.4, 41.4, 43.9, 43.3, 44.5, 45.8, 45.9, 46.4, > 47.6, 45.5, > 42.6, 39.3, 39.6, 42.7, 44.2, 47.1, 48.2, 46.4, 47.8, 49.6, > 54.1, 59.1, > 64.9, 66, 64.4, 58.9, 59.3, 60.2, 58.7, 60, 63.8, 64.9, 66) # > employment > R> K <- c(236.2, 240.2, 248.9, 254.5, 264.1, 273.9, 282.6, > 290.2, 299.4, > 303.3, 303.4, 297.1, 290.1, 285.4, 287.8, 292.1, 300.3, 301.4, 305.6, > 313.3, 327.4, 339, 347.1, 353.5, 354.1, 359.4, 359.3, 365.2, > 363.2, 373.7, > 386, 396.5, 408) # capital > R> klein <- cbind(Y,L,K) > R> klein.data<-data.frame(klein) > R> coef(lm(log(Y)~log(L)+log(K))) > # i used these linearized model's estimated parameters as > initial values > (Intercept) log(L) log(K) > -3.6529493 1.0376775 0.7187662 > R> nls(Y~ALPHA * (L^(BETA1)) * (K^(BETA2)), > data=klein.data, start = > c(ALPHA=-3.6529493,BETA1=1.0376775,BETA2=0.7187662), trace = T) > 6852786785 : -3.6529493 1.0376775 0.7187662 > 1515217 : -0.02903916 1.04258165 0.71279051 > 467521.8 : -0.02987718 1.67381193 -0.05609925 > 346945.7 : -0.5570735 10.2050667 -10.2087997 > Error in numericDeriv(form[[3]], names(ind), env) : > Missing value or an Infinity produced when > evaluating the model > ------------------------------------ > > 1. What went wrong? I think the initial values are not good > enough: How can > i make a grid search? > > 2. How can i estimate C.E.S Production Function [ Y = GAMA * > ((DELTA*K^(-BETA)) + ((1-DELTA)*L^(-BETA)))^(-PHI/BETA) ] > using the same > data? How to get the initial value? >
Dear James, Wettenhall, as far as the CES production function is concerned, you might want to utilise the Kmenta approximation. The following link elucidates this approach and other feasible estimation techniques. http://www.cu.lu/crea/projets/mod-L/prod.pdf HTH, Bernhard > N.B.: The data file is available at http://www.angelfire.com/ab5/get5/klein.txt Any response / help / comment / suggestion / idea / web-link / replies will be greatly appreciated. Thanks in advance for your time. _______________________ Mohammad Ehsanul Karim <[EMAIL PROTECTED]> Institute of Statistical Research and Training University of Dhaka, Dhaka- 1000, Bangladesh ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -------------------------------------------------------------------------------- The information contained herein is confidential and is inte...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
