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kjetil b halvorsen ♦
kjetil b halvorsen ♦
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KD89042
KD89042
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I'm trying to build a regression model that predicts Trump's vote share in a county in the 2016 election, given demographic data about that county. One of the demographic variables I would like to use is the distribution of income in that county; i.e. the % of people in the county earning <$10k, $10k-20k,etc. If I use the % of people in each bin as an independent variable, then intuitively the coefficient estimates for "nearby" bins would have high collinearity and be prone to overfitting. How do I use the prior knowledge that "%<10k" and "%10k-20k" variables should have a similar (but unknown) effect on Trump vote share in my regression?

Edit: To be clear, I would use n-1 variables to represent n income bins.

I'm trying to build a regression model that predicts Trump's vote share in a county in the 2016 election, given demographic data about that county. One of the demographic variables I would like to use is the distribution of income in that county; i.e. the % of people in the county earning <$10k, $10k-20k,etc. If I use the % of people in each bin as an independent variable, then intuitively the coefficient estimates for "nearby" bins would have high collinearity and be prone to overfitting. How do I use the prior knowledge that "%<10k" and "%10k-20k" variables should have a similar (but unknown) effect on Trump vote share in my regression?

I'm trying to build a regression model that predicts Trump's vote share in a county in the 2016 election, given demographic data about that county. One of the demographic variables I would like to use is the distribution of income in that county; i.e. the % of people in the county earning <$10k, $10k-20k,etc. If I use the % of people in each bin as an independent variable, then intuitively the coefficient estimates for "nearby" bins would have high collinearity and be prone to overfitting. How do I use the prior knowledge that "%<10k" and "%10k-20k" variables should have a similar (but unknown) effect on Trump vote share in my regression?

Edit: To be clear, I would use n-1 variables to represent n income bins.

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KD89042
KD89042
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How do I use a distribution as an independent variable in a regression?

I'm trying to build a regression model that predicts Trump's vote share in a county in the 2016 election, given demographic data about that county. One of the demographic variables I would like to use is the distribution of income in that county; i.e. the % of people in the county earning <$10k, $10k-20k,etc. If I use the % of people in each bin as an independent variable, then intuitively the coefficient estimates for "nearby" bins would have high collinearity and be prone to overfitting. How do I use the prior knowledge that "%<10k" and "%10k-20k" variables should have a similar (but unknown) effect on Trump vote share in my regression?