Investigation of Parameter Behaviors in Stationarity of Autoregressive and Moving Average Models through Simulations Investigation of Parameter Behaviors in Stationarity of Autoregressive and Moving Average Models through Simulations

Main Article Content

Akeyede Imam

Abstract

The most important assumption about time series and econometrics data is stationarity. Therefore, this study focuses on behaviors of some parameters in stationarity of autoregressive (AR) and moving average (MA) models. Simulation studies were conducted using R statistical software to investigate the parameter values at different orders (p) of AR and (q) of MA models, and different sample sizes. The stationary status of the p and q are, respectively, determined, parameters such as mean, variance, autocorrelation function (ACF), and partial autocorrelation function (PACF) were determined. The study concluded that the absolute values of ACF and PACF of AR and MA models increase as the parameter values increase but decrease with increase of their orders which as a result, tends to zero at higher lag orders. This is clearly observed in large sample size (n = 300). However, their values decline as sample size increases when compared by orders across the sample sizes. Furthermore, it was observed that the means values of the AR and MA models of first order increased with increased in parameter but decreased when sample sizes were decreased, which tend to zero at large sample sizes, so also the variances.

Article Details

Section
Research Article

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.