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

The FixNA (Shi et al., 2009) and FixNA2 (Matilainen et al., 2017) methods for blind source separation problem. It is used for time series with stochastic volatility. These methods are alternatives to vSOBI method.

1 2 3 4 5 6 7 |

`X` |
A numeric matrix or a multivariate time series object of class |

`k` |
A vector of lags. It can be any non-zero positive integer, or a vector consisting of them. Default is |

`eps` |
Convergence tolerance. |

`maxiter` |
The maximum number of iterations. |

`G` |
Function |

`method` |
The method to be used. The choices are |

`ordered` |
Whether to order components according to their volatility. Default is |

`acfk` |
A vector of lags to be used in testing the presence of serial autocorrelation. Applicable only if |

`original` |
Whether to return the original components or their residuals based on ARMA fit. Default is |

`...` |
Further arguments to be passed to or from methods. |

Assume that a *p*-variate *Y* with *T* observations is whitened, i.e. *Y = S^(-1/2)*(X_t - (1/T)*sum_t(X_t))*, for *t = 1, …, T*,
where *S* is the sample covariance matrix of *X*. The algorithm for method FixNA finds an orthogonal matrix *U* by maximizing

*D_1(U) = sum_k(D_k1(U)) = sum_i(sum_k((1/(T - k))*sum_t[G(u_i' Y_t)*G(u_i' Y_(t + k))])) *

and the algorithm for method FixNA2 finds an orthogonal matrix *U* by maximizing

*D_2(U) = sum_k(D_k2(U))*

*= sum_k(sum_i(|(1/(T - k))*sum_t[G(u_i' Y_t)*G(u_i' Y_(t + k))] - (1/(T - k))^2*sum_t[G(u_i' Y_t)]*sum_t[G(u_i' Y_(t + k))]|)),*

where *i = 1, …, p*, *k = 1, …, K* and *t = 1, …, T*. For function *G(x)* the choices are *x^2* and log(cosh(*x*)).

The algorithm works iteratively starting with `diag(p)`

as an initial value for an orthogonal matrix *U = (u_1, u_2, …, u_p)'*.

Matrix *T_mik* is a partial derivative of *D_mk(U)*, for *m = 1, 2*, with respect to *u_i*.
Then *T_mk = (T_m1k, …, T_mpk)'*, where *p* is the number of columns in *Y*, and *T = sum(T_mk)*.
The update for the orthogonal matrix *U.new = (T_m T_m')^(-1/2)*T_m* is calculated at each iteration step. The algorithm stops when

*||U.new - U.old||*

is less than `eps`

.
The final unmixing matrix is then *W = U S^(-1/2)*.

For `ordered = TRUE`

the function orders the sources according to their volatility. First a possible linear autocorrelation is removed using `auto.arima`

. Then a squared autocorrelation test is performed for the sources (or for their residuals, when linear correlation is present). The sources are then put in a decreasing order according to the value of the test statistic of the squared autocorrelation test. For more information, see `lbtest`

.

A list with class 'bssvol' (inherits from class 'bss') containing the following components:

`W ` |
The estimated unmixing matrix. |

`k ` |
The vector of the used lags. |

`S ` |
The estimated sources as time series object standardized to have mean 0 and unit variances. If |

If `ordered = TRUE`

, then also the following components included in the list:

`fits ` |
The ARMA fits for the components with linear autocorrelation. |

`armaeff ` |
A logical vector. Has value 1 if ARMA fit was done to the corresponding component. |

`linTS ` |
The value of the modified Ljung-Box test statistic for each component. |

`linP ` |
P-value based on the modified Ljung-Box test statistic for each component. |

`volTS ` |
The value of the volatility clustering test statistic. |

`volP ` |
P-value based on the volatility clustering test statistic. |

Markus Matilainen

Hyvärinen, A. (2001), *Blind Source Separation by Nonstationarity of Variance: A Cumulant-Based Approach*, IEEE Transactions on Neural Networks, 12(6): 1471–1474.

Matilainen, M., Miettinen, J., Nordhausen, K., Oja, H. and Taskinen, S. (2017), *On Independent Component Analysis with Stochastic Volatility Models*, Austrian Journal of Statistics, 46(3–4), 57–66.

Shi, Z., Jiang, Z. and Zhou, F. (2009), * Blind Source Separation with Nonlinear Autocorrelation and Non-Gaussianity*, Journal of Computational and Applied Mathematics, 223(1): 908–915.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
library(stochvol)
n <- 10000
A <- matrix(rnorm(9), 3, 3)
# simulate SV models
s1 <- svsim(n, mu = -10, phi = 0.8, sigma = 0.1)$y
s2 <- svsim(n, mu = -10, phi = 0.9, sigma = 0.2)$y
s3 <- svsim(n, mu = -10, phi = 0.95, sigma = 0.4)$y
# create a daily time series
X <- ts(cbind(s1, s2, s3) %*% t(A), end = c(2015, 338), frequency = 365.25)
res <- FixNA(X)
res
coef(res)
plot(res)
head(bss.components(res))
MD(res$W, A) # Minimum Distance Index, should be close to zero
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.