nl.acf | R Documentation |

Plots autocorrelogram for non linear process.

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

`ar` |
Vector of AR coefficients. If |

`ma` |
Vector of MA coefficients. If |

`y` |
Univariate time series. |

`main` |
Character string representing the title for the plot. |

`nlag` |
Maximum lag at which to calculate the acf. If |

`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). |

For the argument `aff`

you have the choice between:
`SN`

, `M`

and `both`

.
`SN`

prints the self-normalized method (see Boubacar Maïnassara and Saussereau) in green,
`M`

prints the modified method introduced by Francq, Roy and Zakoïan (see also Boubacar Maïnassara) in red
and `both`

prints both of the methods.

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

The only value available for the argument `conflevel`

are
0.1, 0.05, 0.025, 0.01 or 0.005.

Boubacar Maïnassara, 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 Maïnassara, 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 Zakoïan, 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.

est<-estimation(p = 1, q = 1, y = CAC40return.sq) 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)

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