Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand

Abstract

By undertaking a large scale simulation study, we demonstrate that the maximum entropy bootstrap (meboot) data generation process can provide accurate and narrow parameter confidence intervals in models with combinations of stationary and nonstationary variables, under both low and high degrees of autocorrelation. The relatively small sample sizes in which meboot performs particularly well make it a useful tool for rolling window estimation. As a case study, we analyze the evolution of the price and income elasticities of import demand for crude oil in Turkey by using quarterly data between 1996-2011. Our approach can be employed to tackle a wide range of macroeconometric estimation problems where small sample sizes are a common issue.