# Heteroskedasticity - i.e. variance specific to each group, j, rather than a single variance for each group-varying parameter?

**URL:** https://discourse.mc-stan.org/t/heteroskedasticity-i-e-variance-specific-to-each-group-j-rather-than-a-single-variance-for-each-group-varying-parameter/9637
**Category:** brms
**Created:** [July 9, 2019, 11:13pm UTC](https://discourse.mc-stan.org/t/heteroskedasticity-i-e-variance-specific-to-each-group-j-rather-than-a-single-variance-for-each-group-varying-parameter/9637 "2019-07-09T23:13:13Z")
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### Author: ![Max\_Mantei](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/max_mantei/32/7912_2.png) [@Max\_Mantei](https://discourse.mc-stan.org/u/Max_Mantei)
#### Post date: [July 17, 2019, 8:08pm UTC](https://discourse.mc-stan.org/t/heteroskedasticity-i-e-variance-specific-to-each-group-j-rather-than-a-single-variance-for-each-group-varying-parameter/9637/6 "2019-07-17T20:08:13Z")

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Remember that the estimates of \sigma are on the log scale, so negatives imply a standard deviation smaller than 1. E.g. the standard deviation for USA in 1994 might be something like \sigma\_\texttt{USA,1994}=\exp(s\_\texttt{USA} + s\_\texttt{1994})=\exp(0.2-0.4)=\exp(-0.2)\approx 0.82.

Ps: just out of the blue… Are you working with FDI data?

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