similar to: Dealing with large nominal predictor in sem package

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 would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

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

Hi - I am trying to fit sem model with intercepts. Here is what I have in my model. Exogeneous vars: x1 (continous), x2 (ordinal), x3 (ordinal), x4(continuous) Endogeneous vars: y1 (continuous), y2 (ordinal), y3 (ordinal) SEM model: x1 -> eta1; x2 -> eta1; x3 -> eta2; x4 -> eta2; eta1 -> y1, eta1 -> y2, eta2 -> y2, eta2 -> y3 However, in these arrow models, I

Hi List I want to carry out structural mode. Following Example l have taken from Basic Econometrics- Damodar Gujarati : Advertising intensity function: Ad/S = a0 + a1M + a2 (CD/S) + a3C + a4C2 + a5Gr + a6Dur – (1) Concentration function: C = b0 + b1 (Ad/S) + b2 (MES/S) -(2) Price-cost margin function: M = c0 + c1(K/S) + c2Gr + c3C + c4GD + c5(Ad/S) + c6 (MES/S)

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 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 All, I am using R 2.11.0 on a Ubuntu machine. I estimated a model using "tsls" from the package "sem". Is there a way to get Newey West standard errors for the parameter estimates? When estimating the model by OLS, I used "NeweyWest" from the package "sandwich" to get HAC standard errors. But, I am not able to use the same method with the results of the

Dear all, Using the tsls package, I noticed that regression lists longer than 64 character are getting truncated. Looking at the original source, tsls.formula <- function(model, instruments, data, subset, weights, na.action, contrasts=NULL){ if (missing(na.action)) na.action <- options()$na.action m <- match.call(expand.dots = FALSE) if (is.matrix(eval(m$data,

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

Dear all, I am trying to estimate simultaneous equation model concerning growth in russian regions. I run the analysis by means of FIML in R sem package. I am not familiar with SEM yet, but I've just got several suitable estimated specifications. Nevertheless, sometimes R gives the following warning message: Warning message: Negative parameter variances. Model is probably underidentified.

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

Hello all, I have an issue where I am generating data and trying to confirm the estimates using a sem. I keep getting an error about the degree of freedom being negative "Error in sem.default(ram, S = S, N = N, raw = raw, data = data, pattern.number = pattern.number, : The model has negative degrees of freedom = -6" Can someone explain this error or tell me what is wrong with my

Hi,I have a fundamental questions that I'm a bit confused. If any guru from this circle could help me out, I would really appreciate.I have a system of equations in which some of the endogs appear on right hand sides of some equations. To solve this, one needs a technique like 2SLS or FIML to circumvent inconsistency of the estimated coefficients. My question is that if I apply the nonlinear

Can somebody help me with the following issue (SEM in R), please:   When I run the model (includes second order models) in R, it gives me the following:   1)       In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars,  :   Could not compute QR decomposition of Hessian. Optimization probably did not converge.   2)       I have aliased parameters and NaNS   or sometimes when

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

First I have to say I am sorry because I have not been so clear in my previous e-mails. I will try to explain clearer what it is my problem. I have the following model: lnP=Sc+Ag+Ag2+Var+R+D In this model the variable Sc is endogenous and the rest are all objective exogenous variables. I verified that Sc is endogenous through a standard Hausman test. To determine this I defined before a new

I know this topic has had plenty of discussion in the last couple of days, but.... I've been trying to compare the effects of different fitted methods for systems of equations (OLS, SUR, 2SLS, 3SLS ) and would like to compare the residual plots (easy) and the qqnorm/qqplot of the fits for the different fitted methdos. For example, qqnorm( residuals( lm( q ~ p + f + a ) ) ) par( new = TRUE )

Hi R-colleagues. In the sem-package i have a problem to introduce hidden variables. As a simple example I take an ordinary factor analysis. The program: cmat=c(0.14855886, 0.05774635, 0.08003300, 0.04900990, 0.05774635, 0.18042029, 0.11213013, 0.03752475, 0.08003300, 0.11213013, 0.24646337, 0.03609901, 0.04900990, 0.03752475, 0.03609901, 0.31702970)

Hi - I am confronting a situation where I have a set of structural equation and one or two of my responses are multinomial. I understand that sem would not deal with the unordered response. So I am thinking of the following two ways: 1. Expanding my response to a new set of binary variables corresponding to each label of my multinomial response. Then use each of these as a separate response in my