similar to: Running a GMM Estimation on dynamic Panel Model using plm-Package

Dear R Experts--- Sorry for all the questions yesterday and today. I am trying to use Yves Croissant's pgmm function in the plm package with Blundell-Bond moments. I have read the Blundell-Bond paper, and want to run the simplest model first, d[i,t] = a*d[i,t-1] + fixed[i] + u[i,t] . no third conditioning variables yet. the full set of moment conditions recommended for system-GMM,

Hi! I am trying to estimate Engel curves using a big sample (>42,000) using lm and taking heteroskedasticity into account by using the summaryHCCM posted here by John Fox (Mon Dec 25 16:01:59 CET 2006). Having used the SIC (with MASS stepAIC) to determine how many powers to use I estimate the model: > # ========================================= > summary.lm(fit.lm.5) Call: lm(formula

Hello, I am trying to conduct a Hausman test to choose between FE estimators and RE estimators. When I try to run: library(plm) fixed <- plm(ROS ~ DiffClosenessC +ZZiele + AggSK + nRedundantStrecken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"),model="within") summary(fixed) fixef(fixed) random <-plm(ROS ~

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 list, has anyone succeeded in using pgmm() on any dataset besides Arellano/Bond's EmplUK, as shown in the vignette? Whatever I try, I eventually get a runtime error because of a singular matrix at various points in pgmm.diff() (which gets called by pgmm()). For example, when estimating a "dynamic" version of the Grunfeld data: data(Grunfeld, package="Ecdat") grun

Hi! I am quite a new user of R. I wanted to ask if there was some package for dynamic panel analysis (with Arneallo-Bond Method) like stata. PLM is for panel analysis but not for dynamic. Best regards, Tanveer

Dear R users, I am trying to simulate some multitrait-multimethod models using the packages sem and psych but whatever I do to deal with models which do not converge I always get stuck and get error messages such as these: "Error in summary.sem(M1) : coefficient covariances cannot be computed" "Error in solve.default(res$hessian) : System ist f?r den Rechner singul?r: reziproke

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’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 -- 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

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

Hi there! Today I tried to estimate models using both plm and pgmm functions, with an interaction between X1 and lag(X2, 1). And I notice two issues. Let "Y=b_1 * X_1 + b_2 * X_2 + b_3 * X_1 * x_2 + e" be our model. 1) When using plm, I got different results when I coded the interaction term with I(X1 * lag(X2, 1)) and when I just saved this multiplication X1 * lag(X2, 1) in a

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

Good day fellow R users: I have routinely received the following message when attempting to estimate a GMM model for a somewhat square panel (N = 20, T = 9-27, Obs = 338) using the pgmm function in the plm package: Error in function (..., deparse.level = 1) : number of rows of matrices must match (see arg 2) So far, I am not wedded to a particular GMM model but what I have used thus far is

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).

dear giovanni--- thanks for answering on r-help to me as well as privately. I very much appreciate your responding. I read the plm vignette. I don't have the book, so I can't consult it. :-(. I am going to post this message now (rather than just email it privately), because other amateurs may have similar questions in the future, and find this message and your answers via google.

Dear Sir or Madam: I am Shaojuan Liao, the 3rd year Ph.D. student from Econ Department, Virginia Tech. I don't know whether it is appropriate to ask you questions on the command pgmm. But I don't know how to deal with the case where all X are exogenous and all T time periods' X can be used as the instrument. Problem 1: I know when X are predetermined, such as Z=[y1,X1,X2, 0,

Dear listserv members, In Croissant (2008) “Panel Data Econometrics in R: The plm Package” the authors seem to indicate that the nlme package for R cannot correctly handle unbalanced panel data: “Moreover, economic panel datasets often happen to be unbalanced (i.e., they have a different number of observations between groups), which case needs some adaptation to the methods and is not

Dear Sir, I am using the package pgmm you build in panel regression. However, I found that when T is 10, N=30, the error would show as following: system is computationally singular: reciprocal condition number But the similar code works well on Stata, so I wonder how I can optimize the algorithm, for example , the inverse matrix optimization ? And I have checked my data as well, no