Hello! Recently, as I was reading a paper, I came across the term kernel-weighted local polynomial smoothing. The regression model itself was a very simple logit model. Can anyone easily explain what exactly that technique is?.. Here is an excerpt from the paper:
We construct Ethnocentrism(j,t) by standardizing survey responses such that 1 reflects pro-immigration sentiment and 0 reflects anti-immigration sentiment. We use kernel-weighted local polynomial smoothing to aggregate the anti-immigrant sentiment for each stateyear. This technique attenuates measurement error arising from different sample sizes across states and combining responses across different questions.
We construct Ethnocentrism(j,t) by standardizing survey responses such that 1 reflects pro-immigration sentiment and 0 reflects anti-immigration sentiment. We use kernel-weighted local polynomial smoothing to aggregate the anti-immigrant sentiment for each stateyear. This technique attenuates measurement error arising from different sample sizes across states and combining responses across different questions.
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