Description Usage Arguments Details Value Warning Author(s) References See Also Examples

Bootstrap/permutation tests of serial and cross dependence for integer or categorical sequences.

1 2 |

`x, y` |
integer or factor time series objects or vectors. ( |

`lag.max` |
maximum lag at which to calculate Srho; default is |

`B` |
number of bootstrap/permutation replications. |

`stationary` |
logical. If |

`plot` |
logical. If |

`quant` |
quantiles to be specified for the computation of the significant lags and the plot of confidence bands. Up to 2 quantiles can be specified. Defaults are 95% and 99%. |

`nor` |
logical. If |

- Univariate version: test for serial dependence
Srho.test(x, lag.max, B = 1000, stationary = TRUE, plot = TRUE, quant = c(0.95, 0.99), nor = FALSE)

- Bivariate version: test for cross dependence
Srho.test(x, y, lag.max, B = 1000, stationary = TRUE, plot = TRUE, quant = c(0.95, 0.99), nor = FALSE)

An object of class "Srho.test", which is a list with the following elements:

`.Data` |
vector of |

`quantiles` |
Object of class |

`test.type` |
Object of class |

`significant.lags` |
Object of class |

`p.value` |
Object of class |

`lags` |
integer vector that contains the lags at which Srho is computed. |

`stationary` |
Object of class |

`data.type` |
Object of class |

`notes` |
Object of class |

Unlike `ccf`

the lag k value returned
by `Srho.test(x,y)`

estimates Srho between `x[t]`

and
`y[t+k]`

. The result is returned invisibly if plot is
TRUE.

Simone Giannerini<[email protected]>

Granger C. W. J., Maasoumi E., Racine J., (2004) A dependence metric for possibly nonlinear processes.
*Journal of Time Series Analysis*, **25(5)**, 649–669.

Maasoumi E., (1993) A compendium to information theory in economics and econometrics.
*Econometric Reviews*, **12(2)**, 137–181.

See also `Srho`

, `Srho.ts`

. The function `Srho.test.ts`

implements the same test for numeric data.

1 2 3 4 5 6 7 8 9 10 11 | ```
set.seed(12)
x <- as.integer(rbinom(n=30,size=4,prob=0.5))
y <- as.integer(rbinom(n=30,size=4,prob=0.5))
z <- as.integer(c(4,abs(x[-30]*2-2))-rbinom(n=30,size=1,prob=1/2))
# no dependence
Srho.test(x,lag.max=4) # univariate
Srho.test(x,y,lag.max=4) # bivariate
# lag 1 dependence
Srho.test(x,z,lag.max=4) # bivariate
``` |

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