<p>Thomas Sargent and Lars Peter Hansen have a series of work on &quot;robustness&quot;, a theory (and technique) that deals directly with potential model misspecifications in the context of macro. [The term &quot;robustness&quot; derives from <a href="https://en.wikipedia.org/wiki/Robust_control" rel="nofollow noreferrer">robust control theory</a>].</p>
<p>Sargent's website has <a href="http://www.tomsargent.com/robustness.html" rel="nofollow noreferrer">a page dedicated to this topic</a>. The two also published a book, <a href="https://press.princeton.edu/titles/8535.html" rel="nofollow noreferrer"><em>Robustness</em></a>, that contains the main results of their work to date. Below is the publisher's description:</p>
<blockquote>
<p>The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted?</p>
<p>Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics.</p>
<p>Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.</p>
</blockquote>

CC BY-SA 4.0
2019-07-25T16:46:48.213Z
2019-07-25T17:07:40.737Z
2020-06-17T09:40:54.643Z
Herr K.
<p>I think it really depends on what your views on model misspecification are. How do you determine whether your model is misspecified? Testing is not really feasible in a Bayesian framework which is increasingly common in macroeconomics nor is it really considered when you have stuctural or theory-based models. Oftentimes the closest people get to dealing with this is to use HAC standard errors to deal with heteroskedasticy and autocorrelation. In empirical VAR's the main approaches are lag selection to address autocorrelation concerns and either differencing or using a Minnesota type prior to address non-stationarity concerns (or going the cointegration route which also addresses non-stationarity). The David Hendry school of thought takes a different approach by incorporating testing for misspecification into the model selection / model design procedure and including non-linear transformations of the data, various indicator saturation procedures and lags.</p>

CC BY-SA 4.0
2019-07-25T16:40:50.203Z
Hi Andrew, could you give some references?
An old man in the sea.
CC BY-SA 4.0
2019-07-25T17:07:50.570Z
Also, structure a bit more your answer, using paragraphs, going into a bit more detail for each topic you refer to?  It's just that you refer to so much, in such a small number of lines...  thanks ;)
An old man in the sea.
CC BY-SA 4.0
2019-07-27T18:42:31.330Z
halbert white wrote a book on mis-specification but I don't think it's related to macro-economics, atleast directly.
mark leeds
CC BY-SA 4.0
2019-07-25T15:50:51.817Z
2019-07-25T15:50:51.817Z
Andrew M
<p>I'm wondering what methods are used in economics, specially in macro, to deal with model misspecification.</p>

<p>I've already searched the web, but I may be missing some methods. Therefore, I ask you for some help.</p>

<macroeconomics><microeconomics><econometrics>
How to deal with misspecification of models in Macroeconomics?
An old man in the sea.
# How to deal with misspecification of models in Macroeconomics?

(Asked by: username_0 on 2019-07-25)

I'm wondering what methods are used in economics, specially in macro, to deal with model misspecification.

I've already searched the web, but I may be missing some methods. Therefore, I ask you for some help.

**Tags:** macroeconomics, microeconomics, econometrics


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## 2 answers

### Answer by username_1 on 2019-07-25. Score: 3

Thomas Sargent and Lars Peter Hansen have a series of work on "robustness", a theory (and technique) that deals directly with potential model misspecifications in the context of macro. [The term "robustness" derives from [robust control theory](https://en.wikipedia.org/wiki/Robust_control)].

Sargent's website has [a page dedicated to this topic](http://www.tomsargent.com/robustness.html). The two also published a book, [*Robustness*](https://press.princeton.edu/titles/8535.html), that contains the main results of their work to date. Below is the publisher's description:

> The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted?
>
> Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics.
>
> Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.


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### Answer by username_2 on 2019-07-25. Score: 0

I think it really depends on what your views on model misspecification are. How do you determine whether your model is misspecified? Testing is not really feasible in a Bayesian framework which is increasingly common in macroeconomics nor is it really considered when you have stuctural or theory-based models. Oftentimes the closest people get to dealing with this is to use HAC standard errors to deal with heteroskedasticy and autocorrelation. In empirical VAR's the main approaches are lag selection to address autocorrelation concerns and either differencing or using a Minnesota type prior to address non-stationarity concerns (or going the cointegration route which also addresses non-stationarity). The David Hendry school of thought takes a different approach by incorporating testing for misspecification into the model selection / model design procedure and including non-linear transformations of the data, various indicator saturation procedures and lags.

> **Comments:**
> **username_0**: Hi username_2, could you give some references?  
> **username_0**: Also, structure a bit more your answer, using paragraphs, going into a bit more detail for each topic you refer to?  It's just that you refer to so much, in such a small number of lines...  thanks ;)  
> **username_3**: halbert white wrote a book on mis-specification but I don't think it's related to macro-economics, atleast directly.  

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