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Last data update: 2014.03.03

R: Entropy Measure Of Serial And Cross Dependence

Srho.ts

R Documentation

Entropy Measure Of Serial And Cross Dependence

Description

Entropy based measure of serial and cross dependence for continuous data. For integer/categorical data see Srho. Implements a normalized version of the Hellinger/Matusita distance. As shown in the references the metric measure is a proper distance.

Usage

Srho.ts(x, y, lag.max = 10, bw = c("reference", "mlcv", "lscv"),
 method = c("integral", "summation"), plot = TRUE,
 maxpts = 0, tol = 0.001)

Arguments

`x, y`	univariate numeric time series object or numeric vectors (`y` is missing in the univariate case).
`lag.max`	maximum lag at which to calculate Srho; default is 10
`bw`	Object of class `"character"`: bandwidth selection method, can be "reference", "mlcv" or "lscv".
`method`	Object of class `"character"`: computation method, can be "integral" or "summation".
`plot`	logical. If `TRUE` (the default) Srho is plotted.
`maxpts`	maximum number of function evaluations, passed to `adaptIntegrate, defaults to 0 (unlimited).`
`tol`	max. tolerance, passed to `adaptIntegrate.`

Details

Univariate version: serial entropy

Srho.ts(x, lag.max = 10,
 bw = c("reference", "mlcv", "lscv"), method = c("summation", "integral"),
 plot = TRUE, maxpts = 0, tol = 0.001)

Bivariate version: cross entropy

Srho.ts(x, y, lag.max = 10,
 bw = c("reference", "mlcv", "lscv"), method = c("summation", "integral"),
  plot = TRUE, maxpts = 0, tol = 0.001)

Value

An object of class "Srho.ts", with the following slots:

`.Data`	Object of class `"numeric"`: contains Srho computed on the data set.
`method`	Object of class `"character"`: computation method
`bandwidth`	Object of class `"character"`: bandwidth selection method.
`lags`	Object of class `"integer"`: contains the lags at which Srho is computed.
`stationary`	Object of class `"logical"`: `TRUE` if the stationary version is computed.
`data.type`	Object of class `"character"`: contains the data type.
`notes`	Object of class `"character"`: additional notes.

Author(s)

Simone Giannerini<simone.giannerini@unibo.it>

References

Granger C. W. J., Maasoumi E., Racine J., (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis, 25(5), 649–669.

Maasoumi E., (1993) A compendium to information theory in economics and econometrics. Econometric Reviews, 12(2), 137–181.

Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, forthcoming.

Examples

set.seed(11)
x <- arima.sim(list(order = c(1,0,0), ar = 0.8), n = 50)
S <- Srho.ts(x,lag.max=5,method="integral",bw="mlcv")

# creates a nonlinear dependence at lag 1
y <- c(runif(1),x[-50]^2*0.8-0.3)
S <- Srho.ts(x,y,lag.max=3,method="integral",bw="mlcv")