ll.edm: Nonlinear forecasting of local polynomial 'empirical dynamic...

In nlts: Nonlinear Time Series Analysis

Description

A function to forcaste a local polynomial ‘empirical dynamic model’.

Usage

ll.edm(x, order, bandwidth, len = NA, deg = 2)

Arguments

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.

Details

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.

Value

A time series with the nonlinear (nonparametric) forecast is returned

References

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

See Also

ll.order

Examples

   data(plodia)

   sim1 <- ll.edm(sqrt(plodia), order=2, bandwidth = 1.5) 

nlts documentation built on May 1, 2019, 8:44 p.m.