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From |
"Rodrigo Alfaro" <ralfaro@bu.edu> |

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

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

Date |
Sun, 27 Nov 2005 11:17:29 -0500 |

Julius, first lagged dependent variable (LDV) with xtreg, fe and xtregar is not the same. xtregar works like Cochrane-Orcutt regression (meaning you have the first observation... read Autocorrelation section in any textbook to learn about it)... for that purpose you have to set what kind of rho will be in your regression (option rhotype)... the default is Durbin-Watson. Details of the formula appear in the manual reference. If the coefficient of your LDV is near to 1 maybe you have a Unit Root case (note that this coefficient is downward-biased if it is positive Nickell 1968). There is a huge (and maybe more than that) about LDV+FE versus Arellano-Bond (1991, GMM estimator), Anderson-Hsiao (1981, IV estimator)... it seems to me that the bias generated by LDV+FE it is not so bad in compare with weak instruments. Hahn-Kuersteiner (2000) propose an asymptotic correction for that parameter... all these is available in Arellano's book. Rodrigo.

----- Original Message ----- From: "Steve Stillman" <stillman@motu.org.nz>

To: <statalist@hsphsun2.harvard.edu>

Sent: Saturday, November 26, 2005 8:48 PM

Subject: st: RE: panel data analysis using xtregar

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

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**Follow-Ups**:**st: RE: Re: RE: panel data analysis using xtregar***From:*Julius Frédéric André <juliusandre@gmail.com>

**References**:**st: RE: panel data analysis using xtregar***From:*"Steve Stillman" <stillman@motu.org.nz>

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