posted on 2018-10-31, 09:15authored byXiaoshan Chen
This thesis revisits the issue of business cycle synchronisation in the Euro area by
utilising time-series models that may overcome some of the drawbacks in the existing
literature. Two major contributions are made to the existing literature of evaluating
cycle synchronisation.
First, instead of identifying turning points from individual macroeconomic timeseries,
as carried out in most studies, this thesis obtains turning points from multivariate
information. It is hoped that including more variables containing business cycle
information in the dating process may produce more accurate turning points and, in turn,
improve the accuracy of measuring cycle correlation. In doing so, both parametric and
non-parametric business cycle dating procedures are used. These include the quarterly
Bry–Boschan (BBQ) algorithm, a single dynamic factor model and the Markov switching
dynamic factor model.
Second, unlike the traditional approach that measures growth cycle synchronisation
in the euro area by calculating pairwise cycle correlations, this thesis analyses the
degree of growth cycle co-movement within a multivariate setting by using a VAR
model with cointegration. [Continues.]
Funding
Loughborough University, Departrment of
Economics.
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Publication date
2009
Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.