panel-data

I'm trying to perform a Difference in Differences (with panel data and fixed effects) analysis using Python and Pandas. I have no background in Economics and I'm just trying to filter the data and run the method that I was told to. However, as far as I could learn, I understood that the basic diff-in-diffs model looks like this: I.e., I am dealing with a multivariable model. Here it follows a simple example in R: https://thetarzan.wordpress.com/2011/06/20/differences-in-differences-estimation-in-r-and-stata/ As it can be seen, the regression takes as input one dependent variable and tree sets

I'm looking for efficiency gains in calculating the (auto)covariance matrix from individual measurements over time t with t, t-1 , etc.. In the data matrix, each row represents an individual and each column represents monthly measurements (the columns are in time order). Similar to the following data (although with some more co-variance). # simulate data set.seed(1) periods <- 70L ind <- 90000L mat <- sapply(rep(ind, periods), rnorm) Below is the (ugly) code I came up with to get the covariance matrix for measurements/ lagged measurements. It takes almost 4 seconds to run. I'm sure that by

I am trying to predict fitted values over data containing NA s, and based on a model generated by plm . Here's some sample code: require(plm) test.data <- data.frame(id=c(1,1,2,2,3), time=c(1,2,1,2,1), y=c(1,3,5,10,8), x=c(1, NA, 3,4,5)) model <- plm(y ~ x, data=test.data, index=c("id", "time"), model="pooling", na.action=na.exclude) yhat <- predict(model, test.data, na.action=na.pass) test.data$yhat <- yhat When I run the last line I get an error stating that the replacement has 4 rows while data has 5 rows. I have no idea how to get predict return a vector of length 5... If instead of

After reading this answer on error in contrasts and reviewing my data I am still stuck with a problem when trying to combine packages 'plm' and 'effects'. This might be impossible as John Fox does not discuss this possibility in his effects document (link is not allowed apparently - Google: "john fox effects package" if you want to have a look). So if it is indeed impossible please tell me. I am running a simple regression on a reduced data set library(plm) ; library(effects) shortdata<-plm.data(shortdata,index=c("ID","Year")) MESS<-plm(paci_to_t ~ paco_to_t + cddom + cddom2,data=shortdata

I have a panel data set in R (time and cross section) and would like to compute standard errors that are clustered by two dimensions, because my residuals are correlated both ways. Googling around I found http://thetarzan.wordpress.com/2011/06/11/clustered-standard-errors-in-r/ which provides a function to do this. It seems a bit ad-hoc so I wanted to know if there is a package that has been tested and does this? I know sandwich does HAC standard errors, but it doesn't do double clustering (i.e. along two dimensions). Frank Harrell's package rms (which used to be named Design ) has a function

I'd like to create a variable containing the value of a variable in the previous year within a group. id date value 1 1 1992 4.1 2 1 NA 4.5 3 1 1991 3.3 4 1 1990 5.3 5 1 1994 3.0 6 2 1992 3.2 7 2 1991 5.2 value_lagged should be missing when the previous year is missing within a group - either because it is the first date within a group (as in row 4, 7), or because there are year gaps in the data (as in row 5). Also, value_lagged should be missing when the current time is missing (as in row 2). This gives: id date value value_lagged 1 1 1992 4.1 3.3 2 1 NA 4.5 NA 3 1 1991 3.3 5.3 4 1 1990 5.3