similar to: GMM: "The covariance matrix of the coefficients is singular"

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

Dear R Help, I am trying to fit a nonlinear model for a mean function $\mu(Data_i, \beta)$ for a fixed covariance matrix where $\beta$ and $\mu$ are low- dimensional. More specifically, for fixed variance-covariance matrices $\Sigma_{z=0}$ and $\Sigma_{z=1}$ (according to a binary covariate $Z $), I am trying to minimize: $\sum_{i=1^n} (Y_i-\mu_(Data_i,\beta))' \Sigma_{z=z_i}^{-1} (Y_i-

Can someone offer some advice on how to properly evaluate a SYMSXP from a .Call ? I have the following in R: variable xn, with an attribute "mu" which references the variable mu in the global environment. I know "references" is a loose term; mu was defined in this fashion as a way to implement deferred binding: foo <- function(x,mu) { attr(x,"mu") <-

Hello -- The question I have is about the gmm() function from the 'gmm' package (v. 1.4-5). The manual accompanying the package says that the gmm() function is programmed to use either of four numerical solvers -- optim, optimize, constrOptim, or nlminb -- for the minimization of the GMM objective function. I wonder whether there is a way to pass controls to a solver used while calling

I’m starting to use the GMM estimator with panel data in R. I´ve read the document «Panel Data Econometrics in R: The plm Package» (Croissant and Millo). In Stata before using the functions lag() or diff() we must sort the data by individual and by time. I would like to know if I have to do something like this in R. If you know any other interesting document about panel data in R please let me

hello I have covariance stacionary proces, and i want to estimate some parameter of this proces via gmm. My problem is with write "g" -function. 0 order autocovariance is not problem 1 and higher order autocavariance are problem, because add order from 0 mean that I "loose" one "observacion" if I have 100 observation and i am going to use mean, variance and first

Hi! I am looking for some insight with this situation: what to do or how to analyze when our models fitted with pgmm never pass Sargant test? With my current dataset, I've been fitting different models and with all possible combinations of lagged instruments, with all possible lag order combinations, but no model passes Sargan test. I can not give up gmm here as I have autocorrelation and

I have a problem in estimating GMM estimators with constraints. How can I do this? [[alternate HTML version deleted]]

Dear everyone: I have to finish my thesis to graduate as Bs. in Economics. I choose to estimate a New Keynesian Phillips Curve (NKPC) for Uruguay using Generalized Moment Method (GMM). I do not know programming or R but I would like to use it. Should I use gee, geepack or gam? Thanks in advance, Sebasti?n. *************************************** ?Hola todos! Para terminiar mi

I have a relatively simple finance application of GMM. Given the moment condition: E[m*R]=0 where m=m[theta] I would like to constrain m>0. Any ideas? [[alternative HTML version deleted]]

Dear, I built the generalized method of moments model to estimate the sales rank in the bookstore using plm package in R. The equation is: data1.gmm <- pgmm(dynformula(lnsales_rank ~ ln_price + avg_ham_rate + avg_spam_rate + num_of_ham+ num_of_spam + ship_code2 +ship_code3 +ship_code4+ ship_code5+ ship_code6 + ship_ code7, lag = list(0, 0, 0, 0,0,0,0,0,0,0,0,0), log =FALSE), data=data,

Hi, I'm trying to use gmm package in order to calculate linear regression (I need to use the gmm for other application and this is a prior test I'm doing). I've defined a function for linear regression with 2 variables (x[,1] holds the y values, while x[,2:3] holds the x values): function(tet, x) { m1 <- (x[,1] - (tet[1] + tet[2] * x[,2] + tet[3] * x[,3])) * x[,2] m2 <-

? Sun, 3 Nov 2024 12:53:52 +0100 Elys?e Aristide <ariel92and at gmail.com> ?????: > Does that mean that I should send a new message? Or is it okay for > this time? No need to post it again. Did it help to replace the as.character() method for formulas provided by 'formula.tools'? I see the problem is already reported to the 'formula.tools' maintainer [*], so there

Hello there!!! Sorry to bother you all with such question and difficulties that I have been facing on. Recently I have been searching for packages to run GMM estimatives with R. I have been searching for such packages for a while, but since I am a new user of R system, my quest so far was unsucessful. That´s why I had decided to ask to this forum. Hope that anyone could help me! I know that

Dear all this is a question of model specification in lme which I'd for which I'd greatly appreciate some guidance. Suppose I have data in long format gene treatment rep Y 1 1 1 4.32 1 1 2 4.67 1 1 3 5.09 . . . . . . . . . . . . 1 4 1 3.67 1 4 2 4.64 1 4 3 4.87 .

Hi Ivan, Thank you for your message. Does that mean that I should send a new message? Or is it okay for this time? Best, Aristide On Fri, Nov 1, 2024, 22:29 Ivan Krylov <ikrylov at disroot.org> wrote: > Hi Aristide and welcome to R-help! > > Your message was a bit mangled [*]. It's best to compose messages to > this mailing list in plain text. Otherwise (when composed in

I need to refresh my memory on Probability Theory, especially on conditional probability. In particular, I want to solve the following two problems. Can somebody point me some good books on Probability Theory? Thank you! 1. Z=X+Y, where X and Y are independent random variables and their distributions are known. Now, I want to compute E(X | Z = z). 2.Suppose that I have $I \times J$ random number

Inthe code below I was trying to to obtain the GMM estimates for CAPM (REGRESSION) for 36 stocks each have 180 observations,however it only gives me one output rather than 36. In SAS i would just put in a *By statement*. I have a variable TICKER that categorize them into 36 groups. *How can I obtain all 36 output instead of just one.* **

Hi everyone, I am using the gmm function from the gmm package and encountered an unexpected error. No model can be estimated if I load formula.tools?I need to restart R each time. Here is a simple reproducible example: *library(gmm)data(Finance)r <- Finance[1:300, 1:10]rm <- Finance[1:300, "rm"]rf <- Finance[1:300, "rf"]z <- as.matrix(r-rf)zm