NxLOCREG - Local or moving polynomial regression

1. Local regression is a non-parametric regression method that combines multiple regression models in a k-nearest-neighbor-based meta-model.
2. Local regression or local polynomial regression, also known as moving regression, is a generalization of moving average and polynomial regression.
3. Its most common methods initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing).
4. Outside econometrics, LOESS is known and commonly referred to as Savitzky–Golay filter. Savitzky–Golay filter was proposed 15 years before LOESS.
5. $\alpha$ is called the smoothing parameter because it controls the flexibility of the LOESS regression function. Large values of ${\displaystyle \alpha }$ produce the smoothest functions that wiggle the least in response to fluctuations in the data. The smaller ${\displaystyle \alpha }$ is, the closer the regression function will conform to the data. Using too small a value of the smoothing parameter is not desirable, however, since the regression function will eventually start to capture the random error in the data.
6. The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variable (X).
7. Observations (i.e. rows) with missing values in X or Y are removed.
8. NxLOCREG is related to NxKREG, but NxLOCRTEG uses the nearest K-NN points to calculate kernel bandwidth and conduct its local regression.
9. The time series may include missing values (e.g. #N/A) at either end.
10. The NxLOCREG() function is available starting with version 1.66 PARSON.

Files Examples

Related Links

• Wikipedia - Moving Average
• Wikipedia - kernel regression

References

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