Heuristic model selection for leading indicators in Russia and Germany:
Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and es...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch Artikel |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2013
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling "holes" in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms |
| Beschreibung: | 1 Online-Ressource (23 p.) 21 x 28cm. |
| DOI: | 10.1787/jbcma-2012-5k49pkpbf76j |
Internformat
MARC
| LEADER | 00000caa a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-13-SOC-061273228 | ||
| 003 | DE-627-1 | ||
| 005 | 20231204121027.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210204s2013 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1787/jbcma-2012-5k49pkpbf76j |2 doi | |
| 035 | |a (DE-627-1)061273228 | ||
| 035 | |a (DE-599)KEP061273228 | ||
| 035 | |a (FR-PaOEC)jbcma-2012-5k49pkpbf76j | ||
| 035 | |a (EBP)061273228 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Savin, Ivan |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Heuristic model selection for leading indicators in Russia and Germany |c Ivan, Savin and Peter, Winker |
| 264 | 1 | |a Paris |b OECD Publishing |c 2013 | |
| 300 | |a 1 Online-Ressource (23 p.) |c 21 x 28cm. | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling "holes" in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms | ||
| 650 | 4 | |a Economics | |
| 700 | 1 | |a Winker, Peter |e MitwirkendeR |4 ctb | |
| 773 | 0 | 8 | |i Enthalten in |t OECD Journal: Journal of Business Cycle Measurement and Analysis |g Vol. 2012, no. 2, p. 67-89 |
| 773 | 1 | 8 | |g volume:2012 |g year:2012 |g number:2 |g pages:67-89 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-13-SOC |q FWS_PDA_SOC |u https://doi.org/10.1787/jbcma-2012-5k49pkpbf76j |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-061273228 |
|---|---|
| _version_ | 1816797353586196480 |
| adam_text | |
| any_adam_object | |
| author | Savin, Ivan |
| author2 | Winker, Peter |
| author2_role | ctb |
| author2_variant | p w pw |
| author_facet | Savin, Ivan Winker, Peter |
| author_role | aut |
| author_sort | Savin, Ivan |
| author_variant | i s is |
| building | Verbundindex |
| bvnumber | localFWS |
| collection | ZDB-13-SOC ZDB-13-SOC-article |
| ctrlnum | (DE-627-1)061273228 (DE-599)KEP061273228 (FR-PaOEC)jbcma-2012-5k49pkpbf76j (EBP)061273228 |
| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1787/jbcma-2012-5k49pkpbf76j |
| format | Electronic Article |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02268caa a22003732 4500</leader><controlfield tag="001">ZDB-13-SOC-061273228</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20231204121027.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210204s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1787/jbcma-2012-5k49pkpbf76j</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)061273228</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP061273228</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(FR-PaOEC)jbcma-2012-5k49pkpbf76j</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)061273228</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">Savin, Ivan</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Heuristic model selection for leading indicators in Russia and Germany</subfield><subfield code="c">Ivan, Savin and Peter, Winker</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Paris</subfield><subfield code="b">OECD Publishing</subfield><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (23 p.)</subfield><subfield code="c">21 x 28cm.</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">Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling "holes" in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Economics</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Winker, Peter</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">OECD Journal: Journal of Business Cycle Measurement and Analysis</subfield><subfield code="g">Vol. 2012, no. 2, p. 67-89</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2012</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:67-89</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-2012-5k49pkpbf76j</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-061273228 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:56:14Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS |
| owner_facet | DE-863 DE-BY-FWS |
| physical | 1 Online-Ressource (23 p.) 21 x 28cm. |
| psigel | ZDB-13-SOC ZDB-13-SOC-article |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | OECD Publishing |
| record_format | marc |
| spelling | Savin, Ivan VerfasserIn aut Heuristic model selection for leading indicators in Russia and Germany Ivan, Savin and Peter, Winker Paris OECD Publishing 2013 1 Online-Ressource (23 p.) 21 x 28cm. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling "holes" in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms Economics Winker, Peter MitwirkendeR ctb Enthalten in OECD Journal: Journal of Business Cycle Measurement and Analysis Vol. 2012, no. 2, p. 67-89 volume:2012 year:2012 number:2 pages:67-89 FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/jbcma-2012-5k49pkpbf76j Volltext |
| spellingShingle | Savin, Ivan Heuristic model selection for leading indicators in Russia and Germany Economics |
| title | Heuristic model selection for leading indicators in Russia and Germany |
| title_auth | Heuristic model selection for leading indicators in Russia and Germany |
| title_exact_search | Heuristic model selection for leading indicators in Russia and Germany |
| title_full | Heuristic model selection for leading indicators in Russia and Germany Ivan, Savin and Peter, Winker |
| title_fullStr | Heuristic model selection for leading indicators in Russia and Germany Ivan, Savin and Peter, Winker |
| title_full_unstemmed | Heuristic model selection for leading indicators in Russia and Germany Ivan, Savin and Peter, Winker |
| title_short | Heuristic model selection for leading indicators in Russia and Germany |
| title_sort | heuristic model selection for leading indicators in russia and germany |
| topic | Economics |
| topic_facet | Economics |
| url | https://doi.org/10.1787/jbcma-2012-5k49pkpbf76j |
| work_keys_str_mv | AT savinivan heuristicmodelselectionforleadingindicatorsinrussiaandgermany AT winkerpeter heuristicmodelselectionforleadingindicatorsinrussiaandgermany |