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From |
Philipp Ehmer <[email protected]> |

To |
"[email protected]" <[email protected]> |

Subject |
st: handling serial correlation in fixed effects estimations |

Date |
Thu, 28 Nov 2013 20:39:00 +0100 |

Hi, I am using panel data on countries (N = 18) and a time period of approx. 25 years (T = 25). It is an unbalanced panel and I have approx. 300 available data points. I would like to employ a fixed effects estimation and I always use the option ", robust" to avoid any kind of heteroskedasticity. A second potential problem I would like to mitigate is serial correlation in the errors. I always look for serial correlation by using the commands "predict resid, e" and then "reg resid l.resid". This shows significant serial correlation in the residuals. My question is: what are my options to get rid of this serial correlation? I know that I can estimate the equation using first differences instead of levels but I would like to use levels. I know that another option is to use a lagged dependent variable as explanatory variable. And I heard of a third way which would be to include time-dummies, i.e. a dummy variable for each year - this sadly doesn't help. First question: am I maybe doing something wrong with those time dummies? Stata omits one so that there is no perfect colinearity, so that seems to work fine so far. Only that when I test for serial correlation in the way that I described above, Stata tells me that there is still substantial serial correlation. Second question: I know that Stata offers some other estimation tools: xtgls, xtpcse and xtregar. What about these tools, do they eliminate serial correlation in the residuals? For instance, when I use xtregar and then again "predict resid, e" and "reg resid l.resid" there is still significant serial correlation. Am I maybe testing it in a wrong way or does xtregar somehow not succeed in eliminating serial correlation? Is it possible that these methodes do not work with my rather small sample? And my third question: my dependent variable seems to be a unit root process. Does this change anything regarding serial correlation? Does this mean, e.g. that I can't use a lagged dependent variable as explanatory variable to get rid of the serial correlation? Any help would be much appreciated! Philipp Ehmer * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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