similar to: limited formula length in tsls

Dear all, I try to conduct a SEM / two stage least squares regression with the following equations: First: X ~ IV1 + IV2 * Y Second: Y ~ a + b X therein, IV1 and IV2 are the two instruments I would like to use. the structure I would like to maintain as the model is derived from economic theory. My problem here is that I have trouble solving the equations to get the reduced form so I can run

Dear friends, I am trying to understand and implement instrumental variables regression using R. I found a small (simple) example here which purportedly illustrates the mechanics (using 2-stage least-squares): http://www.r-bloggers.com/a-simple-instrumental-variables-problem/ Basically, here are the R commands (reproducible example) from that site: # ------ begin R library(AER)

Hi, I have a problem with R-devel and Stataread 2.5. Stataread installs (compiles, at gives no error messages). But typing > library(stataread) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "/usr/local/lib/R/library/stataread/libs/stataread.so": /usr/local/lib/R/library/stataread/libs/stataread.so: undefined symbol: errorcall Error in

Hi there, I hope to build a model Y ~ X1 + X2 + X3 + X4 with X1 has two instrumental variable A and B, and X2 has one instrumental variable A. I have searched the R site and mailling list, and known that the tsls() from sem package and ivreg() from AER package can deal with instrumental variable regression, however, I don't know how to formula the model. Any suggestion will be really

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

Hi, I am new to R, but a fairly `old' user of Stata. I read posts asking about survey methods and large datasets in the archive, so I will not ask those questions again. But some still remain: - R seems to consume more memory given the same set of data, say if I have only a data frame defined, than Stata. Am I right if I think that this is because the object oriented nature of R and can

Hi, I am new to R, but a fairly `old' user of Stata. I read posts asking about survey methods and large datasets in the archive, so I will not ask those questions again. But some still remain: - R seems to consume more memory given the same set of data, say if I have only a data frame defined, than Stata. Am I right if I think that this is because the object oriented nature of R and can

Hello, I try to do a very simple nonlinear regression. The function is y = (b0 + b1*x1 + b2*x2 + b3*x3) * x4^b4 I think I do everything well, but as I set the starting value of b4 to 0 (it is the theoretically sane starting value), it converges very quickly, and to the wrong solution. Wrong in a sense, that 1) we do not expect this and 2) we do not get this on E-Views, Stata and SAS. I do not

Hi, How could I see the source code of functions in an R package? If we type ?function_name , we will see documentations of the function_name. If we type function_name, is what returns just the source code? Could we just save it in an .R file and modify as we want? However, it seems that sometimes the source code is hidden (or stored elsewhere?) As an example, could we see the source

Dear all, Playing with a toy problem, I wondered how much it costs to generate `internal' objects in a function that are not strictly needed. An example: mean(apply(t(matrix(as.vector(x) - x0 / h, , nrow=nrow(x), ncol=ncol(x))), c(2) epakern, dim)) / h^dim It would be more _readable_ if I defined the object inside the apply(), stick it there and rm() after, but my feeling is that creating

Dear all, Being an economics student, I am trying to put together a little tutorial/FAQ/... for those who have (more) background in econometrics rather than in statistics -- just as I have. I'v been looking for a hypothesis testing tool in R (just linear or also nonlinear) for model parameters, but could not find anything so far. I did find a similar but unanswered question in the archives,

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

Dear all, I am running platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 1 minor 3.0 year 2001 month 06 day 22 language R and have my own library path set up having R_LIBS='~/lib/R'; export

Hi,   I'm actually I’m performing a TSLS linear multiple regression on annually data which go from 1971 to 1997. After performing the TSLS regression, I tried to extract the R squared value using “output$r.squared” function and to perform autocorrelation (Durbin Watson and Breush Godfrey) and heterokedasticity tests (Breush-pagan and Goldfeld Quandt)  but I have errors messages. More

Hello !   I’m trying to estimate a system of equation (demand and supply) using the systemfit package.  My program is:   library(systemfit) demand <- tsyud ~ tsyud1 + tsucp + tspo + tssn supply <- tscn ~ tsyn + tsqn + tsksn + tsucp system <- list(demand=eqdemand, learning = eqsupply) labels <- list(demand="eqdemand", learning="eqsupply") inst <- ~ tsupp1 + tsupp2

Hi, I am using tsls function from sem package to estimate a model which includes large number of data. Among its predictors, it has a nominal data which has about 10 possible values. So I expand this parameter into 9-binary-value predictors with the coefficient of base value equals 0. I also have another continuous predictor. The problem is that, whenever I run the tsls, I will get 'System

Hello everyone, I am trying to fit the following model All X. variables are continuous, while the conditions are categoricals. model <- lm(X2

In the vcdExtra package on R-Forge, I have functions and generic methods for calculating log odds ratios for R x C x strata tables. I'd like to define methods for fitting weighted lm()s to the resulting loddsratio objects, but I'm having problems figuring out how to do this generally. # install.packages("vcdExtra", repos="http://R-Forge.R-Project.org")

Hi All, I have a formula which has a factor with NAs in it. I wish to keep these in the model matrix, but the NA information is currently lost (the rows are kept but the NA gets converted to 0). Any ideas as to how I can keep NAs in? e.g. junk <-

Hello, I am trying to replicate the "missing example" of a TSLS estimation in Epple & McCallum (link below) http://wpweb2.tepper.cmu.edu/facultyadmin/upload/ppaper_32774807225408_Epple-McCallum93.pdf According to them, the commands are in: http://www.tepper.cmu.edu/faculty-research/faculty-pages/dennis-epple/simultaneous-equation-econometrics/index.aspx They use the Stata's