search for: kmenta

I am confused by the following description in http://www.maths.lth.se/help/R/.R/library/systemfit/html/hausman.systemfit.html what does the "Not run" mean? if we do not load systemfit, how can we run the following code? ## Not run: library( systemfit ) data( kmenta ) attach( kmenta ) ... I install the package of systemfit, and run the code. I got the warning: > library( systemfit ) > data( kmenta ) > attach( kmenta ) The following object(s) are masked from package:base : q [[alternative HTML version deleted]]

...to MLE in R by Macro Steenbergen http://artsci.wustl.edu/~jmonogan/computing/r/MLE_in_R.pdf Any comments are welcome. In particular I would like to know why the estimate of sigma2 is so different. I would also like to know about the accuracy of the coefficient estimates. ## ols ols <- lm(Kmenta$consump ~ Kmenta$price + Kmenta$income) coef(summary(ols)) ## mle y <- matrix(Kmenta$consump) x <- cbind(1, Kmenta$price, Kmenta$income) ols.lf <- function(theta, y, x) { N <- nrow(y) K <- ncol(x) beta <- theta[1:K] sigma2 <- theta[K+1] e <- y - x%*%beta logl &l...

...imitation, I was just surprised when I attempted to estimate a model, which I'd previously estimated with OLS using 'lm', with 2SLS using 'systemfit' and it didn't accept those transformations like 'lm' does. Here's an example: this is, of course, OK: > data(kmenta) > demand <- q ~ p + d > instr <- ~ d + f > fit1 <- systemfit("2SLS", eqns=list(demand), inst=instr, data=kmenta) But, now if I'd like to estimate a model with logarithm of p as a regressor, an error occurs: > demand2 <- q ~ log(p) + d > fit2 <- system...

Hi I was trying to estimate simultaneous equation system in R using systemfit. I used the following commands >library(systemfit) > data(Kmenta) > attach(Kmenta) >eqDemand<-consump~price+income > eqSupply<-consump~price+farmprice+trend > fitsur<-systemfit("SUR",list(demand=eqDemand, supply=eqSupply)) and got the following error messege Error in systemfit("SUR", list(demand = eqDemand, supply = eqS...

...ion. I know using nlm for a linear model isn't the "optimal" method, but I would like to make sure the parameter estimates and the se's are matching before I attempt more difficult problems. rm(list=ls(all=TRUE)) print( "running nlsystemfit models test at end...") data( kmenta ) attach( kmenta ) ##demand2 <- q ~ d0 + d1 * p + d2 * d supply2 <- q ~ s0 + s1 * p + s2 * f + s3 * a ##system2 <- list( demand2, supply2 ) ##labels <- list( "Demand", "Supply" ) ##inst <- ~ d + f + a ##sv2 <- c(d0=3,s2=2.123,d2=4,s0=-2.123,s3=4.234,d1=4.234,s1...

Hello to everyone, I have 2 problems and would be very pleased if anyone can help me: 1) When I use the package "systemfit" for SUR regressions, I get two different variance-covariance matrices when I firstly do the SUR regression ("The covariance matrix of the residuals used for estimation") and secondly do the OLS regressions. In the manual for "systemfit" on page

Hi there, I am really new to statistics in R and statistics itself as well. My situation: I ran a lot of OLS regressions with different independent variables. (using the lm() function). After having done that, I know there is endogeneity due to omitted variables. (or perhaps due to any other reasons). And here comes the Hausman test. I know this test is used to identify endogeneity. But what I

...al stock GD = measure of geographic dispersion of output Here, Equation (1) & (3) are just identified but equation (2) is underidentified My question is: How to carry out 2sls? l have read rhelp wrt to 2sls, example provided over there is summary(tsls(Q ~ P + D, ~ D + F + A, data=Kmenta)) # demand equation But I still do not have clear idea how to write 2SLS command in R wrt to the example l have mentioned .Can you help me providing the syntax wrt to model which l have mentioned? Regards, Priya Saha [[alternative HTML version deleted]]

...d1 * p + d2 * d supply2 <- q ~ s0 + s1 * p + s2 * f + s3 * a system2 <- list( demand2, supply2 ) sv2 <- c(d0=3,d2=4,d1=4.234,s0=-2.123,s2=2.123,s3=4.234,s1=0.234) ### call the nlm function to get the estimates... estnew <- nlm( knls, sv2, hessian=TRUE, print.level=1, eqns=system2, data=kmenta, fitmethod="OLS" ) My question is why can't I simply call names on the vector (sv2) that is passed into the nlm function to get the parameters? Thanks, Jeff. --- Jeff D. Hamann Hamann, Donald and Associates, Inc. PO Box 1421 Corvallis, Oregon USA 97339-1421 (office) 541-754-1428...

...d1 * p + d2 * d supply2 <- q ~ s0 + s1 * p + s2 * f + s3 * a system2 <- list( demand2, supply2 ) sv2 <- c(d0=3,d2=4,d1=4.234,s0=-2.123,s2=2.123,s3=4.234,s1=0.234) ### call the nlm function to get the estimates... estnew <- nlm( knls, sv2, hessian=TRUE, print.level=1, eqns=system2, data=kmenta, fitmethod="OLS" ) My question is why can't I simply call names on the vector (sv2) that is passed into the nlm function to get the parameters? I was assuming that the names, if available, are always present no matter where they get passed. Is that not correct? Optim give the same re...

I have a large number of troff help files which are automatically converted to .Rd files. Many of these have examples which contain the printed output of the examples. This makes example(functionname) not work. Is there a way to avoid removing all this output by delimiting it in some way such that the output will not appear in R-ex? Secondly, it would be nice if there was a place where output

...the McElroy's measure of fit unless it's NULL. When the option saveMemory = TRUE, the McElroy isn't included, instead it defaults to NA. Thus I am unable to use summary.systemfit. > library(systemfit) > example(systemfit) > surfit2 <- systemfit("SUR",system,data=Kmenta,saveMemory=T) > summary(surfit2) As far as I can tell, this is a result of the following code. > print.summary.systemfit <<--SNIP-->> if (!is.null(x$mcelr2)) { cat("McElroy's R-squared value for the system: ") cat(formatC(x$mcelr2, digits = digits, width = -1)) c...

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,