GetPower: Simulation function to compute power for AR(1) alternative

In mleur: Maximum likelihood unit root test

Description

Compares the empirical power of unit-root tests using simulation. Various non-normal distributions may be selected.

Usage

GetPower(phi, n, NSIM = 1000, tests = c("DF", "MLEp", "MLEn", "MCT"), 
	noiseDist = c("normal", "t", "stable", "GARCH11"), df = 5, 
	ALPHA = 1.5, BETA = 0, alpha = 0.2, beta = 0.7)

Arguments

phi	AR(1) parameter or phi=1 if null is true
n	length of series
NSIM	Number of simulations
tests	available tests include: DF for Dickey-Fuller, MLEp for exact MLE using pivotal, MLEn - exact MLEn using normalized, MCT using Monte-Carlo test
noiseDist	distribution of innovations: "normal" for Gaussian; "t" for t-distribution; "stable" for stable distribution; "GARCH11" for GARCH
df	df for t-distribution
ALPHA	shape parameter of stable distribution in (0,2]
BETA	skewness parameter of stable in [-1,1]
alpha	GARCH(1,1) first parameter
beta	GARCH(1,1) second parameter

Value

List with the following components:

power	vector with estimated power for selected tests
phi	AR(1) parameter value
NSIM	Number of simulations used
MOE	margin of error for level 0.95 c.i.

Author(s)

A.I. McLeod

See Also

mleur, dftest

Examples

GetPower(phi=0.8, n=50, NSIM=100, tests=c("DF", "MLEp"))
 

mleur documentation built on May 1, 2019, 7:31 p.m.