Kernel regression
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The Kernel regression is a non-parametrical technique in statistics to estimate the conditional expectation of random variable.
In any nonparametric regression, the conditional expectation of a variable Y relative to a variable X may be written:
where m is a non-parametric function.
Nadarya (1964) and Watson (1964) proposed to estimate m as a locally weighted average, using a kernel as a weighting function. The Nadarya-Watson estimator is:
where K is a kernel with a bandwith h.
[edit] Statistical implementation
kernreg2 y x, bwidth(.5) kercode(3) npoint(500) gen(kernelprediction gridofpoints)
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