tseriesEntropy (version 0.5-12)

Srho.ts: 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.

Value

.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.

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)

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.

See Also

Srho.test.ts, adaptIntegrate. The function Srho implements the same measure for integer/categorical data.

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")