similar to: coefficients poolability (was: question regarding panel data analysis)

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Good day R-listers, My question is more a statistical question than an R related question, so please bear with me i'm currently applying the chow test of poolability in fact i'm working with panel N=17 T=5 , and my model looks like this : Yit= a0+B1X1+B2X2+B3X3+B4X4+eit My question is the following when i'm Testing for the equality of the coefficients of the unpooled data (the

Hello Cecilia, nice hearing from you again. I must restate a couple of my old hints, though ;^) 1) please always put the authors c/c, as we are not guaranteed to browse through the r-help every day 2) please provide reproducible examples. As example(pooltest) keeps working fine, as do some other cases I tried (Grunfeld data etc.), I don't know what the problem is but evidently your data are

Good day R-users, So if the question may seem easy to many of you but this present a serious issue for me . I'm currently running a panel data analysis i've used the plm package to perform the Tests of poolability as results intercepts and coefficients are assumed different. so my question is should give up the panel analysis in my case or is there any alternative methodology or

Hi everyone! I?m running the pooltest in plm package, like this pooltest(cstfin12~lmaccdiscrz+lcobjur+lliq+lcollateral+ldimensao, data = dados3, model = "within") But I got the following error: Error in FUN(X[[1L]], ...) : insufficient number of observations My data is an unbalanced panel with 20907 observations (6971 individuals and years 2001 to 2007). This is not enough?

Hi all, After writing data in MMF(Memory Map File) using SEXP structure, can i reference in R? If input data is larger than 2GB, Can i reference MMF Data in R? my work environment : R version : 2.11.1 OS : WinXP Pro sp3 Thanks and best regards. Park, Young-Ju from Korea. ---------[ ???????? ???????? ???????? ]---------- ???????? : R-help Digest, Vol 89,

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

The package AER accompanying the forthcoming book "Applied Econometrics with R" by Christian Kleiber and me in the Springer useR! series has (finally!) been released to CRAN: http://CRAN.R-project.org/package=AER It contains some new R functionality o tobit regression convenience interface (to "survival") o instrumental variables regression (two-stage least squares)

The package AER accompanying the forthcoming book "Applied Econometrics with R" by Christian Kleiber and me in the Springer useR! series has (finally!) been released to CRAN: http://CRAN.R-project.org/package=AER It contains some new R functionality o tobit regression convenience interface (to "survival") o instrumental variables regression (two-stage least squares)

You wrote: Ø Dear All, Ø Can anyone provide me with reference notes(or steps) towards analysis of?? (un)balanced panel data in R. Ø Thank you! The "plm" package does panel data analysis in R. See the vignette at: cran.r-project.org/web/packages/plm/vignettes/plm.pdf. There are other similar articles by the same authors, Yves Croissant and Giovanni Millo, and one of these is the

Hi, How could I see the source code of functions in an R package? If we type ?function_name , we will see documentations of the function_name. If we type function_name, is what returns just the source code? Could we just save it in an .R file and modify as we want? However, it seems that sometimes the source code is hidden (or stored elsewhere?) As an example, could we see the source

Dear all I am trying to fit Variable Coefficients Models on Unbalanced Panel Data. I managed to fit such models on balanced panel data (the example from the "plm" vignette), but I failed to do so on my real, unbalanced panel data. I can reproduce the error on a modified example from the vignette: > require(plm) > data("Hedonic") > Hed <- pvcm(mv ~ crim + zn + indus

Hi All, I am doing econometric modeling of panel data (fixed effects). We currently use Eviews to do this, but I have discovered a bug in Eviews 7 and am exploring the use of R to build panel data models / longitudinal data models. I looked at the plm package but do not see how I can incorporate AR terms in the model using the plm package. I have an Eviews model with two AR terms, AR(1) and

Good day R-listers, I'm currently working on a panel data analysis (N=17, T=5), in order to check for the spurious regression problem, i have to ?test for stationarity but i've read somewhere ?that i needn't to test for it as ?my T<10 , what do you think? if yes ?is there any other test ?i have to ?perform in such case (a kind of cointegration test for small T?) Any hint would be

Hi all, what indicates the parameter theta in the summary of a random effect panel model estimated with the plm function? example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) summary(zz) Effects: var std.dev

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 R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

dear readers---I struggled with how to do nice fixed-effects regressions in large economic samples for a while. Eventually, I realized that nlme is not really what I needed (too complex), and all I really wanted is the plm package. so, I thought I would share a quick example. ################ sample code to show fixed-effects models? in R # create a sample panel data set with firms and years

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Dear all R users, I have a small doubt about panel data analysis. My basic understanding on Panel data is a type of data that is collected over time and subjects. Vector Autoregressive Model (VAR) model used on this type of data. Therefore can I say that, one of statistical tools used for analysis of panel data is VAR model? If you clarify my doubt I will be very grateful. Thanks and regards,