Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1100
Title: Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand
Authors: Yalta, Abdullah Talha
Keywords: oil demand
bootstrap
simulation
rolling windows
meboot
Issue Date: Aug-2016
Publisher: Springer
Source: Yalta, A. T. (2016). Bootstrap inference of level relationships in the presence of serially correlated errors: a large scale simulation study and an application in energy demand. Computational Economics, 48(2), 339-366.
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.
URI: https://doi.org/10.1007/s10614-015-9530-7
https://hdl.handle.net/20.500.11851/1100
ISSN: 0927-7099
Appears in Collections:İktisat Bölümü / Department of Economics
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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