An R package for Normality in Stationary Processes

2020/09/22 by Izhar Asael Alonzo Matamoros, Matamoros, Izhar Asael Alonzo, Alicia Nieto-Reyes +1
Computer Science · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Computation (stat.CO) #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2009.10481

openalex publication_date 2020/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Normality is the main assumption for analyzing dependent data in several time series models, and tests of normality have been widely studied in the literature, however, the implementations of these tests are limited. The nortsTest package performs the tests of Lobato and Velasco, Epps, Psaradakis and Vavra and random projection for normality of stationary processes. In addition, the package offers visual diagnostics for checking stationarity and normality assumptions for the most used time series models in several \R packages. The aim of this work is to show the functionality of the package, presenting each test performance with simulated examples, and the package utility for model diagnostic in time series analysis.

Citations

• Testing the null hypothesis of stationarity against the alternative of a unit root
• Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
• Table for Estimating the Goodness of Fit of Empirical Distributions
• Generalized autoregressive conditional heteroskedasticity
• Testing for unit roots in autoregressive-moving average models of unknown order
• An analysis of variance test for normality (complete samples)

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