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. Four normality test are implemented. The Lobato and Velasco's, Epps, Psaradakis and Vavra, and the random projections tests for 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. The package also offers residual diagnostic for linear time series models developed in several packages.
We present four main functions, for testing the hypothesis of
normality in stationary process, the
varvra.test. Additionally, we provide functions
for unit root, seasonality and ARCH effects tests for stationary, and other additional
methods for visual checks using the ggplot2 and forecast packages.
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.
Psaradakis, Z. & Vavra, M. (2017). A distance test of normality for a wide class
of stationary process. Journal of Econometrics and Statistics. 2, 50-60.
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),
Wickham, H. (2008). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
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