<ol>
<li><p>You can also statistically test heteroskedasticity (e.g., White's test: regress squared residuals from OLS on $\hat{y}$ and $\hat{y}^2$, or if you have a simple model, regress $\hat{u}^2$ on $x$ and $x^2$).</p></li>
<li><p>It's unclear what you mean by your "model having heteroskedasticity" (do you mean the error term is heteroskedastic?) and "impacting the prediction". You can always <em>choose to</em> ignore heteroskedasticity if you want. (You seem to do so, as you mentioned OLS.) Or you can have a different estimator (e.g., WLS) for a better (sometimes only slightly better) predictor using the same model. Or you can even have a model that incorporates heteroskedasticity explicitly as in, for example, GARCH models in time series. Please be more specific if you want more help. You might want to read this post for time series: <a href="https://stats.stackexchange.com/questions/175360/forecasting-ar-arch-garch-models">https://stats.stackexchange.com/questions/175360/forecasting-ar-arch-garch-models</a></p></li>
</ol>

CC BY-SA 3.0
2018-04-10T02:23:22.433Z
It is OLS regression. I have the result of residuals. However, how do I detect the heteroskedasticity graphically?
Tom
CC BY-SA 3.0
2018-04-11T13:20:26.017Z
Tom, this is an interesting question in its own right. Post it as a separate question.
BKay
CC BY-SA 3.0
2018-04-12T13:53:24.253Z
If you have a single explanatory variable, plotting the residuals against the explanatory variable would work. If you have multiple regressors, you can plot the residuals against the fitted values.
chan1142
CC BY-SA 3.0
2018-04-09T08:57:35.263Z
2018-04-09T08:57:35.263Z
chan1142
<p>I am making an OLS model. Firstly, I want to confirm that checking heteroskedasticity. 
How to check the presence of heteroskedasticity?
Is it just plot resid against independent variables, right? </p>

<p>Secondly, If the model does have heteroskedasticity, does it impact this OLS model prediction? </p>

<econometrics>
heteroskedasticity OLS model prediction
Tom
# heteroskedasticity OLS model prediction

(Asked by: username_0 on 2018-04-09)

I am making an OLS model. Firstly, I want to confirm that checking heteroskedasticity.
How to check the presence of heteroskedasticity?
Is it just plot resid against independent variables, right?

Secondly, If the model does have heteroskedasticity, does it impact this OLS model prediction?

**Tags:** econometrics


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## 1 answers

### Answer by username_1 on 2018-04-09. Score: 4 | Accepted answer

1. You can also statistically test heteroskedasticity (e.g., White's test: regress squared residuals from OLS on $\hat{y}$ and $\hat{y}^2$, or if you have a simple model, regress $\hat{u}^2$ on $x$ and $x^2$).
2. It's unclear what you mean by your "model having heteroskedasticity" (do you mean the error term is heteroskedastic?) and "impacting the prediction". You can always *choose to* ignore heteroskedasticity if you want. (You seem to do so, as you mentioned OLS.) Or you can have a different estimator (e.g., WLS) for a better (sometimes only slightly better) predictor using the same model. Or you can even have a model that incorporates heteroskedasticity explicitly as in, for example, GARCH models in time series. Please be more specific if you want more help. You might want to read this post for time series: <https://stats.stackexchange.com/questions/175360/forecasting-ar-arch-garch-models>

> **Comments:**
> **username_0**: It is OLS regression. I have the result of residuals. However, how do I detect the heteroskedasticity graphically?  
> **username_2**: username_0, this is an interesting question in its own right. Post it as a separate question.  
> **username_1**: If you have a single explanatory variable, plotting the residuals against the explanatory variable would work. If you have multiple regressors, you can plot the residuals against the fitted values.  

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