Time series analysis under model uncertainty

Jan Hendrik Lohmeyer

Research output: ThesisDoctoral ThesisExternal prepared

273 Downloads (Pure)

Abstract

Central banks analyze economic data in order to uncover the dynamics of the economy and the interdependencies between different economic factors, for example between interest rates and economic growth. This thesis motivates and develops new analysis methods for such economic times series. One of the proposed methods allows the user to explicitly define and take into account the goal of her analysis; another presented method is an extension of a popular tool for economic policy analysis. The thesis illustrates the methods' properties and shows how they compare with other commonly used methods. The results can help central banks and economists to make more informed decisions about which analysis tools to use.
Original languageEnglish
Awarding Institution
  • Maastricht University
Supervisors/Advisors
  • Palm, Franz, Supervisor
  • Urbain, Jean-Pierre, Supervisor
  • Hecq, Alain, Supervisor
Award date24 May 2019
Place of PublicationMaastricht
Publisher
Print ISBNs9789463802963
DOIs
Publication statusPublished - 2019

Keywords

  • Econometrics
  • vector auto regression
  • structural VAR
  • Monetary policy
  • model selection criteria
  • model averaging
  • model uncertainty

Cite this

Lohmeyer, J. H. (2019). Time series analysis under model uncertainty. ProefschriftMaken Maastricht. https://doi.org/10.26481/dis.20190524jl