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  • School of Business, Economics and Law / Handelshögskolan
  • Department of Economics / Institutionen för nationalekonomi med statistik
  • Research Report
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  • School of Business, Economics and Law / Handelshögskolan
  • Department of Economics / Institutionen för nationalekonomi med statistik
  • Research Report
  • Redigera dokument
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Some statistical aspects on methods for detection of turning points in business cycles

Sammanfattning
Statistical and practical aspects on methods for on-line turning point detection in business cycles are discussed. When a method is used on a real data set, there are a number of special data problems to be considered. Among these are: the effect of smoothing, seasonal variation, autoregression, the presence of a trend and problems with multivariate data. Different approaches to these data problems are reviewed and discussed. In a practical situation, another important aspect is the estimation procedure for the parameters of the monitoring system. Three likelihood based methods for turning point detection are compared, one based on a Hidden Markov Model and another including a non-parametric estimation procedure. The three methods are used to analyze an actual data set of a period of the Swedish industrial production. The relative merits of comparing methods by one real data set or by simulations are discussed.
Utgivare
University of Gothenburg
URL:
http://hdl.handle.net/2077/24419
Samlingar
  • Research Report
Fil(er)
gupea_2077_24419_1.pdf (1.283Mb)
Datum
2002-07-01
Författare
Andersson, Eva
Bock, David
Frisén, Marianne
Nyckelord
Business cycles
decision rules
sequential signals
turning points
nonparametric
smoothing
seasonality
autoregressive
optimal
likelihood ratio
Markov switching
regime switching model
Swedish industrial production
Publikationstyp
report
ISSN
0349-8034
Serie/rapportnr.
Research Report
2002:7
Språk
eng
Metadata
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