search for: poolability

Hello. Not an easy question at all, and it has little to do with software, alas! Veeeeeery loosely speaking: if the homogeneity hypothesis is rejected, then, depending on data availability, you may still be able to treat the data like a panel by: a) ignoring the results of the poolability test b) allowing the coefficients to vary. Of course, a) requires some courage while b) requires more degrees of freedom etc.. Some authoritative commentators (Baltagi) stress the advantages of imposing even an uncertain homogeneity hypothesis over resorting to heterogeneous techniques with uncert...

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 last stage) many of my constraints get dropped, this indeed impact the de...

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 neste...

...ni Generali SpA Via Machiavelli 4, 34132 Trieste (Italy) tel. +39 040 671184 fax +39 040 671160 ### original message ### ------------------------------ Message: 106 Date: Thu, 10 Dec 2009 10:53:01 +0000 From: "Cecilia Carmo" <cecilia.carmo at ua.pt> Subject: [R] plm ? tests of poolability ? error: insufficient number of observations To: r-help at r-project.org Message-ID: <web-92270892 at controller.cgpmail.ua.pt> Content-Type: text/plain;charset=iso-8859-1;format="flowed" Hi everyone! I?m running the pooltest in plm package, like this pooltest(cstfin12~lmaccdiscr...

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?

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 transformation i can use instead?? Any hint would be highly appreciated thanks a lot in advance. Ama

...d") Error in FUN(X[[1L]], ...) : insufficient number of observations Would this be expected behaviour for unbalanced data? The vignette warns of several limitations regarding such data, but doesn't mention this specific case as a limitation. I would like to subsequently perform a test of poolability on my real data. Thank you Liviu > sessionInfo() R version 2.10.1 (2009-12-14) x86_64-pc-linux-gnu locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 [5] LC_MONETARY=C LC_MESSAGES=en_GB.UTF-8 [7] LC_PAPER=en_GB.UTF-8...

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

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