NxKREG - Kernel Regression

Returns the (Nadaraya–Watson) kernel regression.

Syntax

NxKREG(X, Y, P, Kernel, H, Optimize, Target, Return)

X

is the x-component of the input data table (a one-dimensional array of cells (e.g. rows or columns)).

Y

is the y-component (i.e., function) of the input data table (a one-dimensional array of cells (e.g., rows or columns)).

P

is the polynomial order (0 = constant, 1 = linear, 2 = Quadratic, 3 = Cubic, etc.), etc.). If missing, P = 0.

Kernel

is the weighting kernel function used with KNN-Regression method : 0(or missing) = Uniform, 1 = Triangular, 2 = Epanechnikov, 3 = Quartic, 4 = Triweight, 5 = Tricube, 6 = Gaussian, 7 = Cosine, 8 = Logistic, 9 = Sigmoid, 10 = Silverman.

Value	Kernel
0	Uniform Kernel (default).
1	Triangular Kernel.
2	Epanechnikov Kernel.
3	Quartic Kernel.
4	Triweight Kernel.
5	Tricube Kernel.
6	Gaussian Kernel.
7	Cosine Kernel.
8	Logistic Kernel.
9	Sigmoid Kernel.
10	Silverman Kernel.

H

is the smoothing parameter (bandwidth) of the kernel density estimator. If missing, a default value of one(1) is assumed.

Optimize

is a flag (True/False) for searching and using optimal integer value K (i.e., number of data points). If missing or omitted, the argument value is assumed False.

target

is the desired x-value(s) to interpolate for (a single value or a one-dimensional array of cells (e.g., rows or columns)).

Return

is a number that determines the type of return value: 0 = Forecast (default), 1 = errors, 2 = Smoothing parameter (bandwidth), 3 = RMSE (CV). NxREG returns forecast/regression value(s) if missing or omitted.

Return	Description
0	Forecast/Regression value(s) (default).
1	Forecast/Regression error(s).
2	Kernel Smoothing parameter (bandwidth).
3	RMSE (cross-validation).

Remarks

1. The number of rows of the response variable (Y) must equal the number of rows of the explanatory variable (X).
2. Observations (i.e., rows) with missing values in X or Y are removed.
3. The NxKREG() function is available starting with version 1.66 PARSON.

Tutorial Video

Files Examples

Related Links

• Wikipedia - Moving Average
• Wikipedia - kernel regression

References

• Pagan, A.; Ullah, A. (1999). Nonparametric Econometrics. Cambridge University Press. ISBN 0-521-35564-8.
• Simonoff, Jeffrey S. (1996). Smoothing Methods in Statistics. Springer. ISBN 0-387-94716-7.
• Li, Qi; Racine, Jeffrey S. (2007). Nonparametric Econometrics: Theory and Practice. Princeton University Press. ISBN 0-691-12161-3.
• Henderson, Daniel J.; Parmeter, Christopher F. (2015). Applied Nonparametric Econometrics. Cambridge University Press. ISBN 978-1-107-01025-3.