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st: AW: Sample selection with autocorrelated errors


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: AW: Sample selection with autocorrelated errors
Date   Sun, 19 Jul 2009 18:47:54 +0200

<> 


Have you had a look at 

Dustmann, Rochina-Barrachina (2007), Selection correction in panel data
models: An application to the estimation of females' wage equations,
Econometrics Journal, 10(2), 263-293

?

HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von kokootchke
Gesendet: Sonntag, 19. Juli 2009 18:28
An: statalist
Betreff: st: Sample selection with autocorrelated errors


So nobody answered my question and I hope it was because it was a difficult
problem and not because my posting was too unclear!

Anyway, I think I found an answer to my question. The Wooldridge textbook
considers the issue of sample selection, truncated samples, and attrition in
panel data in chapter 17 of his book. It's relatively simple to follow and
also considers the case of heteroskedasticity and serially correlated
residuals, in case you are interested.

I still have one more issue in my estimation, but I'll post this message
separately.

Best regards,
Adrian


>> From: kokootchke@hotmail.com
>> To: statalist@hsphsun2.harvard.edu
>> Subject: st: Sample selection with autocorrelated errors
>> Date: Fri, 17 Jul 2009 15:41:25 -0400
>>
>> Hello everyone!
>>
>> I posted the message below earlier but I have decided to change the
subject and make it a bit more general and see if you guys can suggest some
references.
>>
>> As my message below describes, I would like to run a model in which there
is a sample selection component and autocorrelation in the errors. The
problem is that, because of selection, some of the past residuals are not
observed.
>>
>> Also, each observation is a bond issued by a given country, so
observations are "clustered" both by the different countries that issue
these bonds, and by the time period in which they were issued (e.g., a
country may issue 3 bonds in a given quarter-year, and then none for the
next two quarters, and then 2 the following quarter, and so on).
>>
>> Could you suggest how I can address these problems or can you point at
any references?
>>
>> Thank you very much!!
>>
>> Adrian
>>
>> ----------------------------------------
>>> From: kokootchke@hotmail.com
>>> To: statalist@hsphsun2.harvard.edu
>>> Subject: st: ARCH disturbances in truncated samples?
>>> Date: Thu, 16 Jul 2009 17:27:49 -0400
>>>
>>> I am running a model that has the spread of a bond as a dependent
variable and a bunch of domestic and global variables on the right-hand
side. The spread is an inverse measure of the price for which the bond is
sold to potential bondholders and gives a sense of the bond issuer's
creditworthiness (higher spread indicates lower creditworthiness). Examples
of domestic variables are GDP growth, debt/GDP, reserves/imports for each
individual country where the bond issuer resides, while examples of global
variables are the US interest rate and various liquidity and volatility
indices.
>>>
>>> One problem is that I have quarterly macroeconomic data but I only
observe the spread whenever countries actually issue a bond. Countries may
decide not to issue a bond because the market will simply not buy the bond
(for example, in the middle of a crisis) or simply because the country
doesn't want to issue it (one quarter they may issue 3 bonds and the next
quarter they may not need to issue at all).
>>>
>>> Now I am not exactly sure of how to model this other than with a Heckman
selection model (let me know if you have other suggestions). One problem is
that I think my errors are not standard Normal, iid errors... but that I
think they follow some sort of ARCH structure -- for a given country, for
example, the error this period may be related to the error in the previous
period. The problem is that "the previous period" may not be exactly t-1...
but the previous period when there was a bond issue...
>>>
>>> How do I account for this?
>>>
>>> Thank you very much in advance...
>>>
>>> Adrian
>>>
>>>
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