Changing presidential approval: Detecting and understanding change points in interval censored polling data

Understanding how a society views certain policies, politicians, and events can help shape public policy, legislation, and even a political candidate's campaign. This paper focuses on using aggregated, or interval censored, polling data to estimate the times when the public opinion shifts on the US president's job approval. The approval rate is modelled as a Poisson segmented (joinpoint) regression with the EM algorithm used to estimate the model parameters. Inference on the change points is carried out using BIC based model averaging. This approach can capture the uncertainty in both the number and location of change points. The model is applied to president Trump's job approval rating during 2020. Three primary change points are discovered and related to significant events and statements.