Detection of Turning Points in Business Cycles:
Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One o...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch Artikel |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2004
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is a parametric likelihood ratio method. Another includes a non-parametric estimation procedure. The third is based on a Hidden Markov Model. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time [of](to) a correct alarm and the predictive value of an alarm are discussed... |
| Beschreibung: | 1 Online-Ressource (16 p.) |
| DOI: | 10.1787/jbcma-v2004-art6-en |
Internformat
MARC
| LEADER | 00000caa a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-13-SOC-06124533X | ||
| 003 | DE-627-1 | ||
| 005 | 20231204121251.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210204s2004 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1787/jbcma-v2004-art6-en |2 doi | |
| 035 | |a (DE-627-1)06124533X | ||
| 035 | |a (DE-599)KEP06124533X | ||
| 035 | |a (FR-PaOEC)jbcma-v2004-art6-en | ||
| 035 | |a (EBP)06124533X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Andersson, Eva |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Detection of Turning Points in Business Cycles |c Eva, Andersson, David, Bock and Marianne, Frisén |
| 264 | 1 | |a Paris |b OECD Publishing |c 2004 | |
| 300 | |a 1 Online-Ressource (16 p.) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is a parametric likelihood ratio method. Another includes a non-parametric estimation procedure. The third is based on a Hidden Markov Model. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time [of](to) a correct alarm and the predictive value of an alarm are discussed... | ||
| 650 | 4 | |a Economics | |
| 700 | 1 | |a Bock, David |e MitwirkendeR |4 ctb | |
| 700 | 1 | |a Frisén, Marianne |e MitwirkendeR |4 ctb | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of Business Cycle Measurement and Analysis |g Vol. 2004, no. 1, p. 93-108 |
| 773 | 1 | 8 | |g volume:2004 |g year:2004 |g number:1 |g pages:93-108 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-13-SOC |q FWS_PDA_SOC |u https://doi.org/10.1787/jbcma-v2004-art6-en |3 Volltext |
| 912 | |a ZDB-13-SOC | ||
| 912 | |a ZDB-13-SOC-article | ||
| 912 | |a ZDB-13-SOC | ||
| 951 | |a AR | ||
| 912 | |a ZDB-13-SOC | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-13-SOC-06124533X |
|---|---|
| _version_ | 1816797354797301760 |
| adam_text | |
| any_adam_object | |
| author | Andersson, Eva |
| author2 | Bock, David Frisén, Marianne |
| author2_role | ctb ctb |
| author2_variant | d b db m f mf |
| author_facet | Andersson, Eva Bock, David Frisén, Marianne |
| author_role | aut |
| author_sort | Andersson, Eva |
| author_variant | e a ea |
| building | Verbundindex |
| bvnumber | localFWS |
| collection | ZDB-13-SOC ZDB-13-SOC-article |
| ctrlnum | (DE-627-1)06124533X (DE-599)KEP06124533X (FR-PaOEC)jbcma-v2004-art6-en (EBP)06124533X |
| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1787/jbcma-v2004-art6-en |
| format | Electronic Article |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02026caa a22003852 4500</leader><controlfield tag="001">ZDB-13-SOC-06124533X</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20231204121251.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210204s2004 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1787/jbcma-v2004-art6-en</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)06124533X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP06124533X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(FR-PaOEC)jbcma-v2004-art6-en</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)06124533X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Andersson, Eva</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Detection of Turning Points in Business Cycles</subfield><subfield code="c">Eva, Andersson, David, Bock and Marianne, Frisén</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Paris</subfield><subfield code="b">OECD Publishing</subfield><subfield code="c">2004</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (16 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is a parametric likelihood ratio method. Another includes a non-parametric estimation procedure. The third is based on a Hidden Markov Model. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time [of](to) a correct alarm and the predictive value of an alarm are discussed...</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Economics</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bock, David</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Frisén, Marianne</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of Business Cycle Measurement and Analysis</subfield><subfield code="g">Vol. 2004, no. 1, p. 93-108</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2004</subfield><subfield code="g">year:2004</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:93-108</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-13-SOC</subfield><subfield code="q">FWS_PDA_SOC</subfield><subfield code="u">https://doi.org/10.1787/jbcma-v2004-art6-en</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC-article</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-13-SOC-06124533X |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:56:15Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS |
| owner_facet | DE-863 DE-BY-FWS |
| physical | 1 Online-Ressource (16 p.) |
| psigel | ZDB-13-SOC ZDB-13-SOC-article |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | OECD Publishing |
| record_format | marc |
| spelling | Andersson, Eva VerfasserIn aut Detection of Turning Points in Business Cycles Eva, Andersson, David, Bock and Marianne, Frisén Paris OECD Publishing 2004 1 Online-Ressource (16 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is a parametric likelihood ratio method. Another includes a non-parametric estimation procedure. The third is based on a Hidden Markov Model. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time [of](to) a correct alarm and the predictive value of an alarm are discussed... Economics Bock, David MitwirkendeR ctb Frisén, Marianne MitwirkendeR ctb Enthalten in Journal of Business Cycle Measurement and Analysis Vol. 2004, no. 1, p. 93-108 volume:2004 year:2004 number:1 pages:93-108 FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/jbcma-v2004-art6-en Volltext |
| spellingShingle | Andersson, Eva Detection of Turning Points in Business Cycles Economics |
| title | Detection of Turning Points in Business Cycles |
| title_auth | Detection of Turning Points in Business Cycles |
| title_exact_search | Detection of Turning Points in Business Cycles |
| title_full | Detection of Turning Points in Business Cycles Eva, Andersson, David, Bock and Marianne, Frisén |
| title_fullStr | Detection of Turning Points in Business Cycles Eva, Andersson, David, Bock and Marianne, Frisén |
| title_full_unstemmed | Detection of Turning Points in Business Cycles Eva, Andersson, David, Bock and Marianne, Frisén |
| title_short | Detection of Turning Points in Business Cycles |
| title_sort | detection of turning points in business cycles |
| topic | Economics |
| topic_facet | Economics |
| url | https://doi.org/10.1787/jbcma-v2004-art6-en |
| work_keys_str_mv | AT anderssoneva detectionofturningpointsinbusinesscycles AT bockdavid detectionofturningpointsinbusinesscycles AT frisenmarianne detectionofturningpointsinbusinesscycles |