OLS with Lagged DV I am interested in building an OLS model with a lagged (lag 1) DV as a right-side explanatory variable. This is relatively straightforward in R, however my problem is the rest of the data. I have six separate data frames of countries that each measure the same three variables of interest (lets say: Y, $X_1$, $X_2$), and these dataframes fall into one of two categories (1 or 2). 
It has been suggested to me that I pool all of these countries together into one model and include a dummy variable as opposed to running a model on each dataframe. However, I am unclear as to how to pool the time series data in such a way that maintains the structure (i.e., maintaining the categories of  each country). It seems that any configuration will involve multiple variables for each country and not at all what has been suggested.
Is it best to add the variables data together and simply have one summed column of data per category per variable? Or, is it the case that the only data that needs to be in time-series format is the DV and the other data ($X_1$ and $X_2$) can be in non-time-series format? 
 A: When you pool data like this you still treat each one data point as one data point in the pooled data; it is best not to aggregate results at this early stage since that involves a loss of information.  You should be able to obtain a pooled data frame for your countries using some simple manipulations of data frames in R.  If you already have six data frames each containing three columns of the same variables, all you need to do is to add a Country column to each data frame and then use rbind to bind the rows together in a single data frame.  Here is an example of how to do this:
#I will assume you have six data frames called DF1-DF6
#I will take the column names to be VarY, Var1 & Var2

#Add Country variable to each data frame
#Countries used here are just an example
DF1$Country <- rep('USA',     nrow(DF1));
DF2$Country <- rep('England', nrow(DF2));
DF3$Country <- rep('France',  nrow(DF3));
DF4$Country <- rep('Canada',  nrow(DF4));
DF5$Country <- rep('Germany', nrow(DF5));
DF6$Country <- rep('Spain',   nrow(DF6));

#Create pooled data frame DF and convert Country to a factor variable
DF <- do.call("rbind", list(DF1, DF2, DF3, DF4, DF5, DF6));
DF$Country <- factor(DF$Country);

