R: nse: Computation of numerical standard errors in R

nse {nse}

R Documentation

nse: Computation of numerical standard errors in R

Description

nse (Ardia and Bluteau, 2017) is an R package for computing the numerical standard error (NSE), an estimate of the standard deviation of a simulation result, if the simulation experiment were to be repeated many times. The package provides a set of wrappers around several R packages, which give access to more than thirty NSE estimators, including batch means estimators (Geyer, 1992, Section 3.2), initial sequence estimators Geyer (1992, Equation 3.3), spectrum at zero estimators (Heidelberger and Welch, 1981), heteroskedasticity and autocorrelation consistent (HAC) kernel estimators (Newey and West, 1987; Andrews, 1991; Andrews and Monahan, 1992; Newey and West, 1994; Hirukawa, 2010), and bootstrap estimators Politis and Romano (1992, 1994); Politis and White (2004). The full set of estimators is described in Ardia et al. (2018).

Functions

nse.geyer: Geyer NSE estimator.
nse.spec0: Spectral density at zero NSE estimator.
nse.nw: Newey-West NSE estimator.
nse.andrews: Andrews NSE estimator.
nse.hiruk: Hirukawa NSE estimator.
nse.boot: Bootstrap NSE estimator.

Note

Functions rely on the packages coda, mcmc,mcmcse, np, and sandwich.

Please cite the package in publications. Use citation("nse").

Author(s)

David Ardia and Keven Bluteau

References

Andrews, D.W.K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59(3), 817-858.

Andrews, D.W.K, Monahan, J.C. (1992). An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator. Econometrica 60(4), 953-966.

Ardia, D., Bluteau, K., Hoogerheide, L. (2018). Methods for computing numerical standard errors: Review and application to Value-at-Risk estimation. Journal of Time Series Econometrics 10(2), 1-9. doi:10.1515/jtse-2017-0011 doi:10.2139/ssrn.2741587

Ardia, D., Bluteau, K. (2017). nse: Computation of numerical standard errors in R. Journal of Open Source Software 10(2). doi:10.21105/joss.00172

Geyer, C.J. (1992). Practical Markov chain Monte Carlo. Statistical Science 7(4), 473-483.

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.

Hirukawa, M. (2010). A two-stage plug-in bandwidth selection and its implementation for covariance estimation. Econometric Theory 26(3), 710-743.

Newey, W.K., West, K.D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelationconsistent covariance matrix. Econometrica 55(3), 703-708.

Newey, W.K., West, K.D. (1994) . Automatic lag selection in covariance matrix estimation. Review of Economic Studies 61(4), 631-653.

Politis, D.N., Romano, and J.P. (1992). A circular block-resampling procedure for stationary data. In Exploring the limits of bootstrap, John Wiley & Sons, 263-270.

Politis, D.N., Romano, and J.P. (1994). The stationary bootstrap. Journal of the American Statistical Association 89(428), 1303-1313.