similar to: GMM with covariance moment condicion

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

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 <-

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645610 I am estimating a system of seemingly unrelated regressions (SUR) with `gmm::sysGmm` in R. Each of the equations has one unique regressor and one common regressor. The common regressor is a dummy variable indicating the last observation (n-1 zeros followed by 1). I impose a restriction that

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 All, I'm trying to estimate some parameters in my model via GMM using the function gmm(), but I keep getting the message "The covariance matrix of the coefficients is singular". I've changed the moment conditions and the initial value of the parameters, and I still get this message. Are the results valid after receiving this message? Any ideas on how to get rid of it?

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.* **

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and

Generally speaking, this sort of detailed statistical question about a speccial package in R does not get a reply on this general R programming help list. Instead, I suggest you either email the maintainer (found by ?maintainer) or ask a question on a relevant R task view, such as https://cran.r-project.org/web/views/Econometrics.html . (or any other that you judge to be more appropriate).

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

Hello, although I searched for a solution related to my problem I didn?t find one, yet. My skills in R aren?t very large, however. For my Diploma thesis I need to run a GMM estimation on a dynamic panel model using the "pgmm" - function in the plm-Package. The model I want to estimate is: "Y(t) = Y(t-1) + X1(t) + X2(t) + X3(t)" . There are no "normal" instruments

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

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

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]]

Hi, may be simle question, but a do not find it anywhere. Is there same function like max() ,but giving more results. max() give 1number-maximum I need funcion what give p bigest number. many thanks -- View this message in context: http://www.nabble.com/bigest-part-of-vector-tp22188901p22188901.html Sent from the R help mailing list archive at Nabble.com.

If first fit my data column V1 to column V2 using normal "lm" fitting, call it "fit1", then I used "acf(fit1$residuals, type='cov', 40) " function to obtain the autocovariance of the residuals, and then constructed a autocovariance matrix, I chose it to be 40x40. Call this autocovariance matrix B, I then use the following "lm.gls" function to

I am trying to generate a set of data points from a Gaussian mixture model. My mixture model is represented by a data frame that looks like this: > gmm weight mean sd 1 0.3 0 1.0 2 0.2 -2 0.5 3 0.4 4 0.7 4 0.1 5 0.3 I have written the following function that generates the appropriate data: gmm_data <- function(n, gmm) { c(rnorm(n*gmm[1,]$weight, gmm[1,]$mean,

Dear Ivo, dear list, (see: Message: 70 Date: Thu, 26 Mar 2009 21:39:19 +0000 From: ivowel at gmail.com Subject: [R] pgmm (Blundell-Bond) sample needed) I think I finally figured out how to replicate your supersimple GMM example with pgmm() so as to get the very same results as Stata. Having no other regressors in the formula initially drove me crazy. This was a case where simpler models are

Hi guys, I'm trying to do the autocovariance of a moving average but it's giving me errors. Here is my code: > w=rnorm(500,0,1) > v=filter(w, sides=2, rep(1/3,3)) > acf(w, lag.max=20) <=that printed out a nice graph. > acf(v, lag.max=20) Error in na.fail.default(as.ts(x)) : missing values in object thanks a lot. -- View this message in context: