The Influence of Autocorrelated Errors on the Bias of Multilevel Time Series Parameter Estimates

I. O. Azeez, R. A. Ipinyomi

The validity of inferences drawn from statistical test results depends on how well data meet associated assumptions. In a two-level multilevel time series model, the standard assumption that the within-individual (level-1) residuals are uncorrelated are rarely checked or  little information tends to be reported on whether the data satisfy the assumption underlying the statistical techniques used. Using a simulation approach, the consequences of violating the level-1 independence of observations assumption on the parameter estimates of fixed effects and the associate errors due to bias was investigated. It was found that bias which is generally high, increases with increase autocorrelated errors, and Full maximum likelihood (FML) estimates are more biased than Restricted maximum likelihood (REML) estimates.