ptest (version 1.0-8)

simHReg: Simulate harmonic regression models

Description

Simulates a harmonic regression. Possible types of models are normal, t(5), Laplace, cubic and AR1.

Usage

simHReg(n, f, A, B, model = c("Gaussian", "t5", "Laplace", "cubic", "AR1"), phi = 0, sig = 1)

Arguments

n

Length of series.

f

Frequency.

A

Cosine amplitude.

B

Sine amplitude.

model

The model used for generating the error term. See details.

phi

Only used if AR1 error distribution is selected.

sig

The standard error of the series.

Value

Vector of length n, simulated harmonic series.

Details

Generate a harmonic series y with length n, where $y_t = A*cos(2*pi*f*t)+B*sin(2*pi*f*t)+sig*e_t,\ t=1,...,n,$ and e comes from one of the following specified distributions with mean 0 and standard error 1:

Gaussian: A standard normal distribution (i.i.d.).

t5: A t distribution with 5 degrees of freedom (i.i.d., standardized to mean 0 and variance 1).

Laplace: A Laplace (double exponential) distribution (i.i.d., standardized to mean 0 and variance 1).

cubic: A standard normal distribution for e, but $y=y^3$ this time.

AR1: An AR(1) series with autocorrelation paramater phi (standardized to mean 0 and variance 1).

References

McLeod, A.I., Yu, Hao and Krougly, Z. (2007), Algorithms for Linear Time Series Analysis: With R Package, Journal of Statistical Software 23, 5 1-26.

See Also

fitHReg, ptestReg

Examples

#Simulate the harmonic regression model with standard Gaussian error terms
z <- simHReg(10, f=2/10, 1, 2, model="Gaussian",sig=1) #Fourier Frequency
plot(1:10,z,type="b")

#Simulate the AR(1) errors
z <- simHReg(10, f=0/10, 0,0, model="AR1",phi=0.2,sig=1) 
acf(z)