similar to: Inequality constraints in GMM estimation?

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 <-

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?

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

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

Dear guRus, I am doing a logistic regression using restricted cubic splines via rcs(). However, the fitted probabilities should be nondecreasing with increasing predictor. Example: predictor <- seq(1,20) y <- c(rep(0,9),rep(1,10),0) model <- glm(y~rcs(predictor,n.knots=3),family="binomial") print(1/(1+exp(-predict(model)))) The last expression should be a nondecreasing

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 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

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).

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 All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

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

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

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,

Dear R-users, in the last days I have been trying to estimate a normal linear model with equality and inequality constraints. Please find below a simple example of my problem. Of course, one could easily see that, though the constraints are consistent, there is some redundancy in the specific constraints. Nevertheless my actual applications can get much larger and I would not like to manually

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! 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 spatial salinity field s and a model g(s) ~ Xb where the X comes from slightly modified GAM basis functions. I am trying to deal with the following set of requirements: 1. The underlying physics are linear, and plain salinity (the identity link) is the correct response to my covariates. 2. Dispersion (variance or sd) is almost certainly proportional to the mean. 3. The data s(x,y)

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Dear All, I would like to minimise a nonlinear function subject to linear inequality constraints as part of an R program. I have been using the constrOptim function. I have tried all of the methods that come with Optim, but nothing finds the correct solution. If I use the correct solution as the vector of starting values, though, my program does output the correct solution and optimum - the

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