nortsTest-package {nortsTest}

'Assessing Normality of a Stationary Process.'

Description

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited.Seven normality test are implemented. The asymptotic Lobato and Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato and Velasco's sieve bootstrap approximation, El bouch et al., Epps sieve bootstrap approximation and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.

Details

We present several functions for testing the hypothesis of normality in univariate stationary processes, the epps.test, lobato.test, rp.test, lobato-bootstrap.test, epps-bootstrap.test, elbouch.test, and varvra.test. Additionally, the elbouch.test function performs a bivariate normality test when the user provides a second time series. For model diagnostics, we provide functions for unit root, seasonality and ARCH effects tests for stationary, and other methods for visual checks using the ggplot2 and forecast packages.

References

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 process. 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, Elsevier, vol. 75(C), pages 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.