similar to: WLS ins systemfit question

Hi, I added a new function "predict.systemfit" to our package "systemfit" to make it closer to other packages (e.g. lm). Now "R CMD check" complains that the generic function "predict" has only the argument "object", while our function "predict.systemfit" has more arguments. However, the function "predict.lm" has also more

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

I might be sorry for asking this question :-) I have two equations and I tried to estimate them individually with "lm" and "gls", and then in a system (using systemfit) with "OLS", "WLS" and "SUR". Quite surprisingly (for myself at least) the results are identical to the last digit. Could someone (please!) give a hint as to what am I

The 'systemfit' function in systemfit 0.7-3 CRAN package seems to have a problem with formulas that contain transformed (eg. log) variables. If I have my data in a data frame, apparently systemfit doesn't "pass" the information of where the variables should be taken to the transforming function. I'm not entirely sure if this is a bug or just a limitation, I was just

I get the following error message after using the sysfit package's function 'systemfitClassic': Error in data[[eqnVar]] : subscript out of bounds When I do this: MSYS1 <- cbind(Y, Num, F, PO, PD, GO, GD) MigOLS1 <- systemfitClassic("OLS", F ~ PO + PD + GO + GD, eqnVar = "Num", timeVar = "Y", data = MSYS1) and I get this error message: Error in

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

Dear R users, The authors of the systemfit package have released a new version of this package with substantial enhancements. The systemfit package contains functions for fitting simultaneous systems of linear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares

Dear R users, The authors of the systemfit package have released a new version of this package with substantial enhancements. The systemfit package contains functions for fitting simultaneous systems of linear equations using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage Least Squares

Hi there fellow R users, Has anyone got an R example of applying an Ideal demand system, possibly using the library systemfit?? Thanks Wayne Dr Wayne R. Jones Senior Statistician / Research Analyst KSS Limited St James's Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd Seventh Floor St James's Buildings 79 Oxford Street

Dear R users, the systemfit package contains functions for fitting systems of simultaneous equations by various estimation methods (e.g. OLS, SUR, 2SLS, 3SLS). Currently version 0.8 of systemfit is available on CRAN. However, shortly we will upload version 1.0, which is NOT BACKWARD COMPATIBLE. The changes that broke backward compatibility were necessary to make systemfit() more similar to

Dear R users, the systemfit package contains functions for fitting systems of simultaneous equations by various estimation methods (e.g. OLS, SUR, 2SLS, 3SLS). Currently version 0.8 of systemfit is available on CRAN. However, shortly we will upload version 1.0, which is NOT BACKWARD COMPATIBLE. The changes that broke backward compatibility were necessary to make systemfit() more similar to

Hi, I added the "tests" subdirectory and a test file (say "myTest.R") to our "systemfit" package. Up to now I create the "myTest.Rout.save" file with > R CMD BATCH --vanilla myTest.R myTest.Rout.save However, "R CMD check" reports two differences between myTest.Rout.save and the output of myTest.R: a) myTest.Rout.save contains following

Hello, I want to solve a nonlinear 3SLS problem with "nlsystemfit()". The equations are of the form y_it = f_i(x,t,theta) The functions f_i(.) have to be formulated as R-functions. When invoking "nlsystemfit()" I get the error Error in deriv.formula(eqns[[i]], names(parmnames)) : Function 'f1' is not in the derivatives table

Is there an option for running SUR and 2SLS regressions with weighting (I am analysing mortality in towns, hence want to weight by population size) Many thanks Jon Anson -- Yonathan (Jon) Anson Department of Social Work Ben Gurion University of the Negev 84105 Be'er Sheva, Israel. Tel: +972 8 647 93 14(w) +972 8 6489286 (h) 067 233279 (m) Fax: +972 8 647 29 33

I've been working on a new package and I have a few questions regarding the behaviour of the nlm function. I've been (for better or worse) using the nlm function to fit a linear model without suppling the hessian or gradient attributes in the objective function. I'm curious as to why the nlm requires 31 iterations (for the linear model), and then it doesn't work when I try to add

Dear All, I've been trying to run a Weighted Least Squares (WLS) regression: Dependent variables: a 60*200 matrix (*Rit*) with 200 companies and 60 dates for each company Independent variables: a 60*4 matrix (*Ft*) with 4 factors and 60 dates for each factor Weights: a 60*200 matrix (*Wit*) with weights for 200 companies and 60 dates for each company The WLS regression I would like to run

I'd like to model the relationship between m responses Y1, ..., Ym and a single set of predictor variables X1, ..., Xr. Each response is assumed to follow its own regression model, and the error terms in each model can be correlated. My understanding is that although lm() handles vector Y's on the left-hand side of the model formula, it really just fits m separate lm models. What should

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

I am having problem using output of lm() function for further analysing using systemfit package. Basicaly, the problem s following - I generate several formulas using lm() > fo1 <- lm(r98[,2] ~ f98[,1] + f98[,2] + ... + f98[,43]) > fo2 <- lm(r98[,1] ~ f98[,1] + f98[,2] + ... + f98[,43]) and than I want to estimate a general model using package systemfit. > fitsur <-

Hi, I am trying to obtain the heteroskedasticity consitent standard errors (HCSE) after apply 2SLS. I obtain 2SLS through tsls from package sem or systemfit: #### tsls #### library (sem) Reg2SLS <-tsls(LnP~Sc+Ag+Ag2+Var+R+D,~I2+Ag+Ag2+Var+R+D) summary (Reg2SLS) #### systemfit #### library (systemfit) RS <- LnP~Sc+Ag+Ag2+Var+R+D Inst <- ~I2+Ag+Ag2+Var+R+D labels