Despite several tests for normality in stationary processes being proposed in the literature, consistent implementations in programming languages are limited. This package implements seven normality tests: the asymptotic Lobato and Velasco, asymptotic Epps, Psaradakis & Vávra, Lobato & Velasco's sieve bootstrap approximation, Elbouch et al., Epps sieve bootstrap approximation, and the random projections test for univariate stationary processes. Additional diagnostics such as unit root tests, seasonality tests, and ARCH effect tests are also included. The Elbouch test additionally performs bivariate normality testing. Residual diagnostics for linear time series models from several packages are also available.
Maintainer: Asael Alonzo Matamoros asael.alonzo@gmail.com
Authors:
Alicia Nieto-Reyes alicia.nieto@unican.es
Other contributors:
Rob Hyndman Rob.Hyndman@monash.edu [contributor]
Mitchell O'Hara-Wild [contributor]
Trapletti A. [contributor]
Functions provided for univariate normality tests include:
epps.test, lobato.test, rp.test,
lobato-bootstrap.test, epps-bootstrap.test,
elbouch.test, and varvra.test. The elbouch.test function can
perform a bivariate test if a second time series is supplied. Model diagnostics
functions include unit root, seasonality, and ARCH effect tests. Visual checks can
be done with ggplot2 and forecast.
Epps, T.W. (1987). Testing that a stationary time series is Gaussian. The Annals of Statistic, 15(4), 1683-1698. https://projecteuclid.org/euclid.aos/1176350618
Lobato, I., & Velasco, C. (2004). A simple test of normality in time series.
Journal of Econometric Theory, 20(4), 671-689.
doi:https://doi.org/10.1017/S0266466604204030
Psaradakis, Z. & Vávra, M. (2017). A distance test of normality for a wide class
of stationary processes. Journal of Econometrics and Statistics, 2, 50-60.
doi:https://doi.org/10.1016/j.ecosta.2016.11.005
Nieto-Reyes, A., Cuesta-Albertos, J., & Gamboa, F. (2014). A random-projection based test of Gaussianity for stationary processes. Computational Statistics & Data Analysis, 75(C), 124-141.
Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the
forecast package for R. Journal of Statistical Software, 26(3), 1-22.
doi:10.18637/jss.v027.i03
Wickham, H. (2008). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Useful links: