pwsd {blocklength} | R Documentation |
Politis and White (2004) Spectral Density "PWSD" Automatic Block-Length Selection
Description
Run the Automatic Block-Length selection method proposed by Politis and White
(2004) and corrected in Patton, Politis, and White (2009). The method is
based on spectral density estimation via flat-top lag windows of Politis and
Romano (1995). This code was adapted from b.star
to add
functionality and include correlogram support including an S3 method,
see Hayfield and Racine (2008).
Usage
pwsd(
data,
K_N = NULL,
M_max = NULL,
m_hat = NULL,
b_max = NULL,
c = NULL,
round = FALSE,
correlogram = TRUE
)
Arguments
data |
an |
K_N |
an integer value, the maximum lags for the auto-correlation,
|
M_max |
an integer value, the upper-bound for the optimal number of lags,
|
m_hat |
an integer value, if set to |
b_max |
a numeric value, the upper-bound for the optimal block-length.
Defaults to |
c |
a numeric value, the constant which acts as the significance level
for the implied hypothesis test. Defaults to |
round |
a logical value, if set to |
correlogram |
a logical value, if set to |
Value
an object of class 'pwsd'
References
Andrew Patton, Dimitris N. Politis & Halbert White (2009) Correction to "Automatic Block-Length Selection for the Dependent Bootstrap" by D. Politis and H. White, Econometric Review, 28:4, 372-375, DOI: doi: 10.1080/07474930802459016
Dimitris N. Politis & Halbert White (2004) Automatic Block-Length Selection for the Dependent Bootstrap, Econometric Reviews, 23:1, 53-70, DOI: doi: 10.1081/ETC-120028836
Politis, D.N. and Romano, J.P. (1995), Bias-Corrected Nonparametric Spectral Estimation. Journal of Time Series Analysis, 16: 67-103, DOI: doi: 10.1111/j.1467-9892.1995.tb00223.x
Tristen Hayfield and Jeffrey S. Racine (2008). Nonparametric Econometrics: The np Package. Journal of Statistical Software 27(5). DOI: doi: 10.18637/jss.v027.i05
Examples
# Generate AR(1) time series
sim <- stats::arima.sim(list(order = c(1, 0, 0), ar = 0.5),
n = 500, innov = rnorm(500))
# Calculate optimal block length for series
pwsd(sim, round = TRUE)
# Use S3 Method
b <- pwsd(sim, round = TRUE, correlogram = FALSE)
plot(b)