nse: Computation of Numerical Standard Errors in R

Summary

nse is an R package (R Core Team (2016)) 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 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),Flegal and Jones (2010)), 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),Politis and Romano (1994),Politis and White (2004)). The full set of methods available is presented in Ardia, Bluteau, and Hoogerheide (2016) together with several examples of applications of NSE in econometrics and finance. The latest version of the package is available at 'https://github.com/keblu/nse'.

References

Andrews, D. W. K. 1991. “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.” Econometrica 59 (3): 817–58. doi:10.2307/2938229.

Andrews, D. W. K., and J Christopher Monahan. 1992. “An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator.” Econometrica 60 (4): 953–66. doi:10.2307/2951574.

Ardia, D., K. Bluteau, and Lennart F. Hoogerheide. 2016. “Comparison of Multiple Methods for Computing Numerical Standard Errors: An Extensive Monte Carlo Study.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741587.

Flegal, J. M., and G. L. Jones. 2010. “Batch Means and Spectral Variance Estimators in Markov Chain Monte Carlo.” Annals of Statistics 38 (2): 1034–70. doi:10.1214/09-aos735.

Geyer, C. J. 1992. “Practical Markov Chain Monte Carlo.” Statistical Science 7 (4): 473–83. doi:10.1214/ss/1177011137.

Heidelberger, Philip, and Peter D. Welch. 1981. “A Spectral Method for Confidence Interval Generation and Run Length Control in Simulations.” Communications of the ACM 24 (4): 233–45. doi:10.1145/358598.358630.

Hirukawa, Masayuki. 2010. “A Two-Stage Plug-in Bandwidth Selection and Its Implementation for Covariance Estimation.” Econometric Theory 26 (3): 710–43. doi:10.1017/s0266466609990089.

Newey, Whitney K, and Kenneth D West. 1987. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 55 (3): 703–8. doi:10.2307/1913610.

———. 1994. “Automatic Lag Selection in Covariance Matrix Estimation.” Review of Economic Studies 61 (4): 631–53. doi:10.3386/t0144.

Politis, Dimitris N, and Joseph P Romano. 1992. “A Circular Block-Resampling Procedure for Stationary Data.” In Exploring the Limits of Bootstrap, 263–70. John Wiley & Sons.

———. 1994. “The Stationary Bootstrap.” Journal of the American Statistical Association 89 (428): 1303–13. doi:10.2307/2290993.

Politis, Dimitris N, and Halbert White. 2004. “Automatic Block-Length Selection for the Dependent Bootstrap.” Econometric Reviews 23 (1): 53–70. doi:10.1081/etc-120028836.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.