nse.spec0 | R Documentation |

Function which calculates the numerical standard error with the spectrum at zero estimator.

nse.spec0( x, type = c("ar", "glm", "daniell", "modified.daniell", "tukey-hanning", "parzen", "triweight", "bartlett-priestley", "triangular", "qs"), lag.prewhite = 0, welch = FALSE, steep = FALSE )

`x` |
A numeric vector. |

`type` |
Method to use in estimating the spectral density function, among |

`lag.prewhite` |
Prewhite the series before analysis (integer or |

`welch` |
Use Welch's method (Welsh, 1967) to estimate the spectral density. |

`steep` |
Use steep or sharp version of the kernel (Phillips et al., 2006) (only available for type: |

Welsh's method use 50% overlap and 8 sub-samples.
The method `"ar"`

estimates the spectral density using an autoregressive model,
`"glm"`

using a generalized linear model Heidelberger & Welch (1981),
`"daniell"`

uses daniell window from the **R** kernel function,
`"modified.daniell"`

uses daniell window the **R** kernel function,
`"tukey-hanning"`

uses the tukey-hanning window,
`"parzen"`

uses the parzen window,
`"triweight"`

uses the triweight window,
`"bartlett-priestley"`

uses the Bartlett-Priestley window,
`"triangular"`

uses the triangular window, and
`"qs"`

uses the quadratic-spectral window,

This kernel based variance estimator apply weights to smooth out the spectral density using a kernel and takes the spectral density at frequency zero which is equivalent to the variance of the serie. Bandwidth for the kernel is automatically selected using cross-validatory methods (Hurvich, 1985).

The NSE estimator.

`nse.spec0`

relies on the packages `coda`

; see the documentation of this package for more details.

David Ardia and Keven Bluteau

Heidelberger, P., Welch, Peter D. (1981).
A spectral method for confidence interval generation and run length control in simulations.
*Communications of the ACM* **24**(4), 233-245.

Phillips, P. C., Sun, Y., & Jin, S. (2006).
Spectral density estimation and robust hypothesis testing using steep origin kernels without truncation.
*International Economic Review*, **47**(3), 837-894.

Welch, P. D. (1967),
The use of Fast Fourier Transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms.
*IEEE Transactions on Audio and Electroacoustics*, **AU-15**(2): 70-73,

Hurvich, C. M. (1985).
Data-driven choice of a spectrum estimate: extending the applicability of cross-validation methods.
*Journal of the American Statistical Association*, **80**(392), 933-940.

## Not run: n = 1000 ar = 0.9 mean = 1 sd = 1 set.seed(1234) x = c(arima.sim(n = n, list(ar = ar), sd = sd) + mean) nse.spec0(x = x, type = "parzen", lag.prewhite = 0, welch = TRUE, steep = TRUE) ## End(Not run)

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