GMM

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GMM

Rmetrics mailing list
Apologies if this question is irrelevant for this group.
Does using HAC standard errors (Newey and West: package sandwich) in OLS regression make using GMM (package: GMM) redundant?
 Regards,Pankaj K Agarwal
+91-98397-11444http://in.linkedin.com/in/pankajkagarwal/
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Re: GMM

mark leeds
Hi : Yes but GMM is used more for either A) when the OLS assumptions are
not true ( i.e autocorrelation or heteroscedasticity. ) or B) you have a
function that is not
necessarily linear like it is in the case of OLS.

Also, Achim could definitely say more on this but there are various
techniques for the construction of HAC estimators so using sandwich may not
necessarily give the same results as GMM even in the OLS case.











On Mon, Jun 27, 2016 at 2:17 PM, Pankaj K Agarwal via R-SIG-Finance <
[hidden email]> wrote:

> Apologies if this question is irrelevant for this group.
> Does using HAC standard errors (Newey and West: package sandwich) in OLS
> regression make using GMM (package: GMM) redundant?
>  Regards,Pankaj K Agarwal
> +91-98397-11444http://in.linkedin.com/in/pankajkagarwal/
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R questions
> should go.

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Re: GMM

Eric Zivot-2
In reply to this post by Rmetrics mailing list
No
OLS is a special case of GMM where the number of moment conditions is the same as the number of parameters. In this case the efficient weight matrix does not matter for estimation but does matter for the calculation of an estimate of the asymptotic variance matrix of the OLS parameters. This is what HAC standard errors do in the sandwich function vcovHAC()

-----Original Message-----
From: R-SIG-Finance [mailto:[hidden email]] On Behalf Of Pankaj K Agarwal via R-SIG-Finance
Sent: Monday, June 27, 2016 11:17 AM
To: R-sig-finance <[hidden email]>
Cc: H.K Pradhan <[hidden email]>
Subject: [R-SIG-Finance] GMM

Apologies if this question is irrelevant for this group.
Does using HAC standard errors (Newey and West: package sandwich) in OLS regression make using GMM (package: GMM) redundant?
 Regards,Pankaj K Agarwal
+91-98397-11444http://in.linkedin.com/in/pankajkagarwal/
        [[alternative HTML version deleted]]

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Re: GMM

mark leeds
Thanks Eric for your explanation. But I'm a little confused. I understand
that in
the case of OLS, the system is perfectly identified. But are you saying that

A) if he used lm and sandwich, then he won't get a correct answer

B) that if he uses GMM, he won't get a correct answer  because it's not
designed
for perfectly identfied systems. ( i.e: there is no weighting matrix ).

C) if he uses both, he'll get different answers.

Thanks.

And to the person who asked the question originally, below is a short but
sweet intro to GMM.

http://lipas.uwasa.fi/~sjp/Teaching/gmm/lectures/gmmc3.pdf







On Tue, Jun 28, 2016 at 12:55 PM, Eric Zivot <[hidden email]> wrote:

> No
> OLS is a special case of GMM where the number of moment conditions is the
> same as the number of parameters. In this case the efficient weight matrix
> does not matter for estimation but does matter for the calculation of an
> estimate of the asymptotic variance matrix of the OLS parameters. This is
> what HAC standard errors do in the sandwich function vcovHAC()
>
> -----Original Message-----
> From: R-SIG-Finance [mailto:[hidden email]] On
> Behalf Of Pankaj K Agarwal via R-SIG-Finance
> Sent: Monday, June 27, 2016 11:17 AM
> To: R-sig-finance <[hidden email]>
> Cc: H.K Pradhan <[hidden email]>
> Subject: [R-SIG-Finance] GMM
>
> Apologies if this question is irrelevant for this group.
> Does using HAC standard errors (Newey and West: package sandwich) in OLS
> regression make using GMM (package: GMM) redundant?
>  Regards,Pankaj K Agarwal
> +91-98397-11444http://in.linkedin.com/in/pankajkagarwal/
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R questions
> should go.
>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R questions
> should go.
>

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Re: GMM

Rmetrics mailing list
In reply to this post by Eric Zivot-2
Respected Eric SirTo rephrase what you said (just to test my understanding of it): If the OLS moment conditions are perfectly identified, and HAC standard errors are used, the output (standard errors) will be identical to those from GMM. Am i right sir? I am using a linear model which is a CAPM variant with an additional squared market premium term. In this case, will not the GMM and OLS with HAC give same results?Your comment will be invaluable sir. Regards,Pankaj K Agarwal


 

    On Tuesday, 28 June 2016 10:27 PM, Eric Zivot <[hidden email]> wrote:
 
 

 No
OLS is a special case of GMM where the number of moment conditions is the same as the number of parameters. In this case the efficient weight matrix does not matter for estimation but does matter for the calculation of an estimate of the asymptotic variance matrix of the OLS parameters. This is what HAC standard errors do in the sandwich function vcovHAC()

-----Original Message-----
From: R-SIG-Finance [mailto:[hidden email]] On Behalf Of Pankaj K Agarwal via R-SIG-Finance
Sent: Monday, June 27, 2016 11:17 AM
To: R-sig-finance <[hidden email]>
Cc: H.K Pradhan <[hidden email]>
Subject: [R-SIG-Finance] GMM

Apologies if this question is irrelevant for this group.
Does using HAC standard errors (Newey and West: package sandwich) in OLS regression make using GMM (package: GMM) redundant?
 Regards,Pankaj K Agarwal
+91-98397-11444http://in.linkedin.com/in/pankajkagarwal/
    [[alternative HTML version deleted]]

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