weakARMA (version 1.0.3)

nl.acf: Autocorrelogram

Description

Plots autocorrelogram for non linear process.

Usage

nl.acf(
  ar = NULL,
  ma = NULL,
  y,
  main = NULL,
  nlag = NULL,
  conflevel = 0.05,
  z = 1.2,
  aff = "both"
)

Arguments

ar

Vector of AR coefficients. If NULL, we consider a MA process.

ma

Vector of MA coefficients. If NULL, we consider an AR process.

y

Univariate time series.

main

Character string representing the title for the plot.

nlag

Maximum lag at which to calculate the acf. If NULL, it is determinate by $nlag=min(10log(n))$ where n is the number of observation.

conflevel

Value of the confidence level, 5% by default.

z

Zoom on the graph.

aff

Specify the method between SN, M and both (see in Details).

Value

An autocorrelogram with every autocorrelations from 1 to a lag max, and with methods you choose to print.

Details

For the argument aff you have the choice between: SN, M and both. SN prints the self-normalized method (see Boubacar Ma<U+00EF>nassara and Saussereau) in green, M prints the modified method introduced by Francq, Roy and Zako<U+00EF>an (see also Boubacar Ma<U+00EF>nassara) in red and both prints both of the methods.

References

Boubacar Ma<U+00EF>nassara, Y. 2011, Multivariate portmanteau test for structural VARMA models with uncorrelated but non-independent error terms Journal of Statistical Planning and Inference, vol. 141, no. 8, pp. 2961-2975.

Boubacar Ma<U+00EF>nassara, Y.and Saussereau, B. 2018, Diagnostic checking in multivariate ARMA models with dependent errors using normalized residual autocorrelations , Journal of the American Statistical Association, vol. 113, no. 524, pp. 1813-1827.

Francq, C., Roy, R. and Zako<U+00EF>an, J.M. 2005, Diagnostic Checking in ARMA Models with Uncorrelated Errors, Journal of the American Statistical Association, vol. 100, no. 470, pp. 532-544.

Lobato, I.N. 2001, Testing that a dependant process is uncorrelated. J. Amer. Statist. Assos. 96, vol. 455, pp. 1066-1076.

Examples

# NOT RUN {
est<-estimation(p = 1, q = 1, y = CAC40return.sq)
# }
# NOT RUN {
nl.acf(ar = est$ar, ma = est$ma, y = CAC40return.sq, main = "Autocorrelation of an ARMA(1,1) 
residuals of the CAC40 return square", nlag = 20)
# }
# NOT RUN {
# }