Dear Marco,
This seems to just be crying out for rolling your own Tobit using
Stata's ml command, but the autoregressive solution can't use ml
method lf, which makes the programming a tad more difficult. If you
did, though, you wouldn't need to do a Cochrane-Orcut two-step; you
could estimate rho simultaneously with everything else.
Peace,
Daniel Lawson
Assistant Professor of Economics
Drew University
http://users.drew.edu/dlawson
On Nov 28, 2006, at 11:17 AM, marco stampini wrote:
> Thank you Scott. I will try to answer myself, with what I found
> out, and try to stimulate more discussion.
>
> The command intreg can be used to address heteroscedasticity. I
> copy the code below.
>
> I think that the same could be exploited to address
> autocorrelation, but I doubt that the code below is correct. Intreg
> allows modelling the variance of the error term as function of some
> variables. Would modelling the variance as a function of the lagged
> dependent variable be equivalent to a Cochrane-Orcut correction of
> autocorrelation in OLS?
>
> Any help/comment would be much appreciated. Thank you.
> Marco Stampini
>
>
> *the tobit
> xi:tobit y $X, ll(0)
>
> *some necessary variables
> gen y2 = y
> replace y2 = . if y<=0
> gen y3 = y
> replace y3 =0 if y<=0
>
> *intreg which replicates the tobit
> xi:intreg y2 y3 $X
>
> *intreg which replicates the tobit and accounts for heteroscedasticity
> xi:intreg y2 y3 $X, r
>
> *intreg which replicates the tobit, accounts for heteroscedasticity
> *and MAYBE(??????) takes care of autocorrelation
> xi:intreg y2 y3 $X, r het(y_lag)
>
>
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
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