# panel data unit root tests

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## panel data unit root tests

 When finally got some time to do some coding, I started and stopped right after. The stationary test is a good starting point because it demonstrates how we should be able to move the very basic R matrices. I have a real- world small N data set with rows: id(n=1)---t1---variable1 ... id=(N=20)---T=21---variable1 Thus, a good test case. For first id I was considering something like this: lag <- as.integer(lags) lags.p <- lags + 1 id <- unique(group) id.l <- length(id) y.l <- length(y) yid.l <- length(y)/id.l   if (lag > yid.l -2)         stop("\nlag too long for defined cross-sections.\n") #for (i in id) {   lagy <- y[2:yid.l]       lagy.em <- embed(lagy, lags)   id.l <- length(id)   dy <- diff(y)[1:yid.l-1]       dy.em <- embed(dy, lags) #     } print(levinlin(ws, year, id, lags = 3)) Couldn't figure the loop over units out but with N = 1 the data transformation seemed to work just fine. Now we should pool the new variables within the panel and regress y over yt-1 and dy-t1 +....+ dy-t-j with, say, BIC doing the job for d's (H0: y-1 ~ 0) for each in the panel. Now the above example puts the right-hand on columns, and if we are dealing with panel models in general, we should store the new variables together with dX's, which should then give clues to IV estimator with e.g. orthogonal deviations, e.g. k <- y ~ yt-1 + x + as.factor(id)). So one confusing part is the requirement of some big storage base for different matrices doing the IV business with lags/levels - the amount of instruments can be enormous with possibly calculation problems in a GMM dynamic panel estimator a la Arellano & Bond. Therefore one should code the theoretically relevant instruments beforehand with various transformation matrices. Thus, should I start to study something that can be done with the newly added SparseM package? Regards, Jukka Ruohonen. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html