search for: pcse

Dear R Community, I am currently facing this seemingly obscure Problem with Panel Corrected Standard Errors (PCSE) following Beck & Katz (1995). As the authors suggest, I regressed a linear model (tmodel) with lm() with option "na.action=na.exclude" (I have also tried other options here). My dataframe is organized in pooled times series fashion, but with various missing values along the spaci...

Dear list, I am trying to replicate an econometrics study that was orginally done in Stata. (Blanton and Blanton. 2009. A Sectoral Analysis of Human Rights and FDI: Does Industry Type Matter? International Studies Quarterley 53 (2):469 - 493.) The model I try to replicate is in Stata given as xtpcse total_FDI lag_total ciri human_cap worker_rts polity_4 market income econ_growth log_trade fix_dollar fixed_xr xr_fluct lab_growth english, pairwise corr(ar1) According to the paper, this is an OLS regression with panel corrected standard errors including a lagged dependent variable (lag_total is...

...xxxxxxx xxxxxxxxx location2 week2 xxx xxxxxxx xxxxxxxxx My DV variable was mean-centered - for each location and I am using this mean-centered DV as the DV in lm. Also, I am using the mean of each location as a weight when I run this regression. Would it still be correct to calculate pcse for this regression? I am not sure because pcse uses the group identifier - so am I not somehow double-counting things? Thank you very much! -- Dimitri Liakhovitski Ninah Consulting www.ninah.com

Just two clarifying questions about the package "pcse". Argument "groupN": It should be a factor that tells us to what subgroup each record belongs, right? Argument "groupT" should be a vector that contains the time identifier. Can it be just a factor (e.g., 1, 2, 3, etc.) - or does it have to be in the date format? Thank y...

...es with this same kind of problem, but I can't understand the answers. Can one of you try to explain this to me? Here's my example. Given a regression model and a variable, I want to use unstack() on the vector of residuals and make some magic with the result. But unstack hates me. PCSE <- function (tmodel,groupVar) { myres1 <- resid(tmodel) resUnstacked <- unstack(myres1, form = myres1 ~ groupVar)); E <- as.matrix(resUnstacked) SIGMA <- (1/nrow(E))*(t(E) %*% E) OMEGA <- diag(x=1, nrow=nrow(E), ncol=nrow(E)) %x% SIGMA X <- model.matrix(tmodel) X...

Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on time series models but they all seem focused on predicting a single variable from its previous values. Can anyone explain to me how to detect and get round autocorrelation? Is there a pa...

Hello everyone I appologize for my lack of experience in statistical methods. I am an R user begginer and I am running a logit model using "zelig" and "pcse" packages. I will go to the point and is that Im having problems with interpreting the results of my models.. It is really simple (I guess for the most advanced scholars) however I really dont understand how to interpret the coefficients. here is my output Call: zelig(formula = newtrst ~ stf...