Economics Job Market Rumors Topic: How useful is Bayesian econometrics?
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics
Economics Job Market Rumors Topic: How useful is Bayesian econometrics?en-USTue, 30 May 2023 16:36:31 +0000http://bbpress.org/?v=1.0.2<![CDATA[Search]]>q
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Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8777666
Thu, 02 Mar 2023 14:48:07 +0000Economist8777666@https://www.econjobrumors.com/<blockquote><p>Anyone using STAN to implement or do you do it by hand
</p></blockquote>
<p>C++
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8777598
Thu, 02 Mar 2023 14:10:22 +0000Economist8777598@https://www.econjobrumors.com/<blockquote><blockquote>
<blockquote>If parameters have infinite dimensions, frequentist methods completely collapse. You can't compare it to Bayesian methods, because there is nothing to compare it to.
</p></blockquote>
<p>Are you drunk? Have you ever picked up book on non-parametric inference?
</p></blockquote>
<p><a href="https://doi.org/10.1016/S0304-4076(99)00044-5" rel="nofollow">https://doi.org/10.1016/S0304-4076(99)00044-5</a>
</p></blockquote>
<p>Irrelevant to quoted point.
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8777580
Thu, 02 Mar 2023 13:54:47 +0000Economist8777580@https://www.econjobrumors.com/<blockquote><p>When you have large parameter space, moving to Bayesian is indeed helpful
</p></blockquote>
<p>Large parameter space or many parameters?
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8777042
Thu, 02 Mar 2023 06:29:11 +0000Economist8777042@https://www.econjobrumors.com/<p>Anyone using STAN to implement or do you do it by hand
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8776851
Thu, 02 Mar 2023 03:54:14 +0000Economist8776851@https://www.econjobrumors.com/<p>When you have large parameter space, moving to Bayesian is indeed helpful
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8245306
Fri, 26 Aug 2022 16:34:41 +0000Economist8245306@https://www.econjobrumors.com/<blockquote><blockquote>If parameters have infinite dimensions, frequentist methods completely collapse. You can't compare it to Bayesian methods, because there is nothing to compare it to.
</p></blockquote>
<p>Are you drunk? Have you ever picked up book on non-parametric inference?
</p></blockquote>
<p><a href="https://doi.org/10.1016/S0304-4076(99)00044-5" rel="nofollow">https://doi.org/10.1016/S0304-4076(99)00044-5</a>
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8245128
Fri, 26 Aug 2022 15:39:49 +0000Economist8245128@https://www.econjobrumors.com/<blockquote><blockquote>
<blockquote>Under regularity conditions, if the parameters have a finite dimension then the Bayesian posteriors asymptotically shrink to the maximum likelihood estimator, and therefore to the parameter of interest. Moreover, any centrality measure of the posterior (conditional mean, or mode) has the same asymptotic distribution as maximum likelihood. In practice, there is absolutely no advantage in one over another, they work the same. Any other statement is just cheap talk of illiterate people.<br />
However, if the parameters have infinite dimension (a nonparametric curve modeled by sieves), Bayesian estimators can have important failures (inconsistent), there are well known examples, and only in some specific cases the Bayesian nonparametric estimators work well.</p></blockquote>
<p>Agree with second paragraph. Disagree with first paragraph. Bayesian and frequentist methods can have dramatically different finite sample behavior - and in real life N is finite. Quoting a bunch of asymptotic results is sweeping this fact under the rug.
</p></blockquote>
<p>If parameters have infinite dimensions, frequentist methods completely collapse. You can't compare it to Bayesian methods, because there is nothing to compare it to.
</p></blockquote>
<p>Are you drunk? Have you ever picked up book on non-parametric inference?
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8244894
Fri, 26 Aug 2022 14:17:44 +0000Economist8244894@https://www.econjobrumors.com/<blockquote><blockquote>Under regularity conditions, if the parameters have a finite dimension then the Bayesian posteriors asymptotically shrink to the maximum likelihood estimator, and therefore to the parameter of interest. Moreover, any centrality measure of the posterior (conditional mean, or mode) has the same asymptotic distribution as maximum likelihood. In practice, there is absolutely no advantage in one over another, they work the same. Any other statement is just cheap talk of illiterate people.<br />
However, if the parameters have infinite dimension (a nonparametric curve modeled by sieves), Bayesian estimators can have important failures (inconsistent), there are well known examples, and only in some specific cases the Bayesian nonparametric estimators work well.</p></blockquote>
<p>Agree with second paragraph. Disagree with first paragraph. Bayesian and frequentist methods can have dramatically different finite sample behavior - and in real life N is finite. Quoting a bunch of asymptotic results is sweeping this fact under the rug.
</p></blockquote>
<p>If parameters have infinite dimensions, frequentist methods completely collapse. You can't compare it to Bayesian methods, because there is nothing to compare it to.
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8243756
Fri, 26 Aug 2022 02:21:07 +0000Economist8243756@https://www.econjobrumors.com/<blockquote><p>Under regularity conditions, if the parameters have a finite dimension then the Bayesian posteriors asymptotically shrink to the maximum likelihood estimator, and therefore to the parameter of interest. Moreover, any centrality measure of the posterior (conditional mean, or mode) has the same asymptotic distribution as maximum likelihood. In practice, there is absolutely no advantage in one over another, they work the same. Any other statement is just cheap talk of illiterate people.</p>
<p>However, if the parameters have infinite dimension (a nonparametric curve modeled by sieves), Bayesian estimators can have important failures (inconsistent), there are well known examples, and only in some specific cases the Bayesian nonparametric estimators work well.</p></blockquote>
<p>Agree with second paragraph. Disagree with first paragraph. Bayesian and frequentist methods can have dramatically different finite sample behavior - and in real life N is finite. Quoting a bunch of asymptotic results is sweeping this fact under the rug.
</p>Economist on "How useful is Bayesian econometrics?"
https://www.econjobrumors.com/topic/how-useful-is-bayesian-econometrics/page/5#post-8243673
Fri, 26 Aug 2022 01:35:53 +0000Economist8243673@https://www.econjobrumors.com/<blockquote><p>I'm the OP. I cannot believe the thread has made it so far. Wow.
</p></blockquote>
<p>Thank you for your service
</p>