Bubbles and crises: Replicating the Anundsen et al. (2016) results

Fu, B

Bubbles and crises: Replicating the Anundsen et al. (2016) results

Fu, B

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Journal of Applied Econometrics, 2019, 34 (5), pp. 822 - 826
Issue Date:: 2019-08

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Field	Value	Language
dc.contributor.author	Fu, B	en_US
dc.date.available	2019-02-05	en_US
dc.date.issued	2019-08	en_US
dc.identifier.citation	Journal of Applied Econometrics, 2019, 34 (5), pp. 822 - 826	en_US
dc.identifier.issn	0883-7252	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138795
dc.description.abstract	This paper both narrowly and widely replicates the results of Anundsen et al. (Journal of Applied Econometrics, 2016, 31(7), 1291–1311). I am able to reproduce the same results as theirs. Furthermore, I find that allowing for time‐varying parameters of early warning system models can considerably improve the in‐sample model fit and out‐of‐sample forecasting performance based on an expanding window forecasting exercise.	en_US
dc.language	en	en_US
dc.publisher	Wiley	en_US
dc.relation.ispartof	Journal of Applied Econometrics	en_US
dc.relation.isbasedon	10.1002/jae.2695	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Econometrics	en_US
dc.title	Bubbles and crises: Replicating the Anundsen et al. (2016) results	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	34	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1402 Applied Economics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true	en_US
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

This paper both narrowly and widely replicates the results of Anundsen et al. (Journal of Applied Econometrics, 2016, 31(7), 1291–1311). I am able to reproduce the same results as theirs. Furthermore, I find that allowing for time‐varying parameters of early warning system models can considerably improve the in‐sample model fit and out‐of‐sample forecasting performance based on an expanding window forecasting exercise.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/138795