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From |
"Steve Stillman" <stillman@motu.org.nz> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: panel data analysis using xtregar |

Date |
Sun, 27 Nov 2005 14:48:22 +1300 |

Julius. It is not exactly clear what your model is here, but, in general, panel data models with lagged dependent variables cannot be consistently estimated by directly entering the lagged variable as a RHS variable. It sounds like you should have a look at xtabond and the user-written xtabond2 for your model. Cheers, Steve -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Julius Frédéric André Sent: Sunday, November 27, 2005 7:26 AM To: statalist@hsphsun2.harvard.edu Subject: st: panel data analysis using xtregar Dear statalist, Currently researching balanced panel data using insolvency prediction estimates for different companies and regressing them (among other controlling, time-variant variables) on time dummies to evaluate the effects of a policy introduction in a certain year. I am using xtreg, fe for evaluation, the panel is described by t=12 and i=110. I presume autocorrelation of the insolvency predictor (ie. the dependent variable) and therefore constructed a dataset with a lagged variable for the insolvency predictor, hence also had to eliminate the first period of the original dataset. It turned out that the estimator of the lagged variable is indeed highly significant. Now I also consider using xtregar. Being new to stata (also to panel data and time series analysis), however, I unfortunately could not find the underlying formula used by Stata up to now. Would such an autoregressive model be similar to the approach I did manually with introducing a lagged variable and deleting the values of the first period for each I? The stata result being different for the AR(1) model and the fixed-effect model including a lagged variable do not corroborate this assumption, so I assume some other underlying model. I know that an AR appproach incorporates past values, but does this mean the past error term, the past dependent or the past independent variable (or all of them)? It would be of great help if someone could provide a formula here or give a brief statement if I should resort to xtregar at all if I assume autocorrelation anyways (so that the applying AR is simply not necessary anymore). Additionally, I would like to backup my decision to include a lagged variable in the model by testing on autocorrelation, using a Durbin-Watson substitute for panel data, such as Bhargava et al. I know that Stata can calculate this statistic when using xtregar, however, it seems to me that the reported output is the result AFTER applying the AR (1) model, indicating only the presence or absence (or degree) of autocorrelation left after using the AR model? Is this correct, or does the test statistic indicate autocorrelation before using the AR model? Lastly, I am not sure whether I could also use the Baltagi-Wu test score alternatively, seeing to it that I have a balanced dataset. Is this test statistic only working with unbalanced data? The three preceding issues certainly are still on a somewhat basic level, however, I would definitely appreciate useful comments on any or all of these! Thank you very much in advance, JULIUS ANDRE * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: Re: RE: panel data analysis using xtregar***From:*"Rodrigo Alfaro" <ralfaro@bu.edu>

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