<p>Could you give an example of the situation when if you input wrong (economic-wise, but not mathematically) parameters the model could fit/forecast better? </p>

CC BY-SA 3.0
2015-09-21T15:18:00.567Z
You are not asking about spurious regressions, are you?
http://www.tylervigen.com/spurious-correlations
They fit well but have 0 forecasting power. Do you mean things that are not "wrong" but unexpected? Like if male underwear sales rise it signals the end of an economic depression?
Giskard
CC BY-SA 3.0
2015-09-21T22:27:43.500Z
@denesp no. What I'm trying to find out is the possibility of for example we have Y=Model(X), and X is determined by some economics conditions which are not necessary for the Model to be solved/worked with. Then by disregarding those 'extra' economics conditions, and focusing only on the solely mathematical ones, we could find a better fit for the observed data Y.
An old man in the sea.
CC BY-SA 3.0
2015-09-21T22:58:56.640Z
I guess this has to depend on how you define better fit. (Like $R^2$ or adjusted $R^2$.) It seems to me you can never get a better model by throwing away information/data.
Giskard
CC BY-SA 3.0
2015-09-22T08:42:51.097Z
@denesp https://en.wikipedia.org/wiki/Overfitting
An old man in the sea.
<macroeconomics><econometrics>
Macroeconometric models with wrong input but better fit/forecast
An old man in the sea.
# Macroeconometric models with wrong input but better fit/forecast

(Asked by: username_0 on 2015-09-19)

Could you give an example of the situation when if you input wrong (economic-wise, but not mathematically) parameters the model could fit/forecast better?

**Tags:** macroeconomics, econometrics

> **Comments:**
> **username_1**: You are not asking about spurious regressions, are you? http://www.tylervigen.com/spurious-correlations They fit well but have 0 forecasting power. Do you mean things that are not "wrong" but unexpected? Like if male underwear sales rise it signals the end of an economic depression?  
> **username_0**: @denesp no. What I'm trying to find out is the possibility of for example we have Y=Model(X), and X is determined by some economics conditions which are not necessary for the Model to be solved/worked with. Then by disregarding those 'extra' economics conditions, and focusing only on the solely mathematical ones, we could find a better fit for the observed data Y.  
> **username_1**: I guess this has to depend on how you define better fit. (Like $R^2$ or adjusted $R^2$.) It seems to me you can never get a better model by throwing away information/data.  
> **username_0**: @denesp https://en.wikipedia.org/wiki/Overfitting  

---

## 0 answers

