Lisa McQuarrie

Title: Autoregressive mixed effects models and an application to annual income of cancer survivors
Date: April 26, 2021
Time: 1:00 pm (PST)
Location: Remote delivery


Yearly observations of annual income are often strongly autocorrelated, with the autocorrelation remaining after the observed explanatory variables are adjusted for. Consequently, longitudinal models used to analyze income should allow for residual autocorrelation. We explore two of the most common such models: 1. the autoregressive error model, which is a linear mixed effects model with an AR(1) covariance structure for the error term, and 2. the autoregressive response model, which is a linear mixed effects model that uses lagged values of the response variable as additional explanatory variables. We also contrast these models with a linear mixed effects model with independent errors, to determine how a model that assumes an independent error structure compares in fit to models that allow for an autocorrelated error structure. The theoretical properties of these models are explored and illustrated using a simulation study. Additionally, the three models are applied to a data set containing observations of income and sociodemographic variables on a sample of breast cancer survivors for a series of years. We aim to determine the short and long-term effect of a breast cancer diagnosis on a survivor’s annual net income.