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, optimize 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). If missing or omitted, NxREG returns forecast/regression value(s).
Return Description 0 Forecast/Regression value(s) (default) 1 Forecast/Regression error(s) 2 Kernel Smoothing parameter (badwidth) 3 RMSE (cross-validation)
Remarks
- The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variable (X).
- Observations (i.e. rows) with missing values in X or Y are removed.
- The NxKREG() function is available starting with version 1.66 PARSON.
Files Examples
Related Links
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.
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