sdPercentiles: Test the significance of signal from rolling correlation...

In dLagM: Time Series Regression Models with Distributed Lag Models

Test the significance of signal from rolling correlation analysis

Description

Implements the approach of Gershunov et al. (2001) to test the significance of signal from rolling correlation analysis.

Usage

sdPercentiles(n = 150, cor = 0.5, width = 5, N = 500, 
              percentiles = c(.05, .95))

Arguments

n	The length of the series in the rolling correlation analysis.
cor	The magnitude of raw correaltion betweeen two time series in the rolling correlation analysis.
width	Window length of the rolling correlation analysis.
N	Number of Monte Carlo replications for simulations.
percentiles	Percentiles to be reported for the Monte Carlo distribution of standard deviations of rolling correlations for the given window width.

Details

N samples of correlated white noise series are generated with a magnitude of cor; rolling correlations analysis is applied with the window length of width; Monte Carlo distribution of standard deviations of rolling correlations are generated; and desired percentiles of the MC distribution of standard deviations are reported (Gershunov et al. 2001).

Value

rollCorSd.limits

Percentiles of MC distribution of standard deviations of rolling correlations as the test limits.

Author(s)

Haydar Demirhan

Maintainer: Haydar Demirhan <[e-mail]>

References

Gershunov, A., Scheider, N., Barnett, T. (2001). Low-Frequency Modulation of the ENSO-Indian Monsoon Rainfall Relationship: Signal or Noise? Journal of Climate, 14, 2486 - 2492.

Examples

# sdPercentiles(n = 50, cor = 0.5, width = 5, N = 50, 
#              percentiles = c(.025, .975))

dLagM documentation built on Oct. 2, 2023, 9:07 a.m.