nortsTest-package: 'Assessing Normality of Stationary Process.'
In nortsTest: Assessing Normality of Stationary Process
Description
Details
References
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. 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.
Details
We present four main functions, for testing the hypothesis of
normality in stationary process, the epps.test, lobato.test,
rp.test, and 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.
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. & Vavra, 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.
nortsTest documentation built on Aug. 16, 2021, 5:06 p.m.
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Description Details References
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. 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.
Details
We present four main functions, for testing the hypothesis of
normality in stationary process, the epps.test, lobato.test,
rp.test, and 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.
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. & Vavra, 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.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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