Description Usage Arguments Details Value References See Also Examples
A function to forcaste a local polynomial ‘empirical dynamic model’.
1 |
x |
A time series without missing values. |
order |
The order for the nonparametric (local polynomial) autoregression. |
bandwidth |
The bandwidth for the nonparametric (local polynomial) autoregression. |
len |
The length of the predicted time-series. If NA the length of the training time series will be used. |
deg |
The degree of the local polynomial. |
The function produces a nonlinear (nonparametric) forecast using the conditional mean method of Fan et al (1996). A Gaussian kernel is used for the local polynomial autoregression.
The bandwidth and order is best estimated with the
ll.order
-function.
Missing values are NOT permitted.
If deg
is set to 0, the forecast uses the Nadaraya-Watson (locally
constant) estimator of the conditional expectation against lagged-abundances.
A time series with the nonlinear (nonparametric) forecast is returned
Fan, J., Yao, Q., & Tong, H. (1996) Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika, 83, 189-206. https://doi.org/10.1093/biomet/83.1.189
Loader, C. (1999) Local Regression and Likelihood. Springer, New York. https://doi.org/10.2307/1270956
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