Tseries: An R Package for Stationarity Tests in Time Series Data

Abstract

The stationary data tests in time series data are the most common idea to create the model with an autoregressive (AR) model, moving average (MA) model, autoregressive moving average (ARMA) model, and autoregressive integrated moving average (ARIMA) model. In this chapter, we present the tseries package in the R program to investigate stationary data on the time series. This package offers Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips-Perron (PP) tests. Three tests also provide the statistics test and p-value to decide the acceptance or rejection of the null hypothesis. This chapter also aims to present the calculation of these tests proposes step-by-step and the available coding commands under similar time series data. The performance of these tests considers the probability of type I error and the power of the test. The time series data in this study is simulated from the stationary in term of random walk process and non-stationary in term of MA model. This simulation is also conducted to recommend that the user select the appropriate stationarity tests. The R program employs to simulate and analyzes data on 1,000 replications for several sample sizes. The simulation results showed that the ADF and PP tests could control the best probability of type I error on non-stationary time series data. For the power of the test, ADF and PP tests give the highest power of the test on stationary time series data.