Adaptive Trees: a new approach to economic forecasting:
The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the cont...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2020
|
| Schriftenreihe: | OECD Economics Department Working Papers
no.1593 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the continuous change in the distribution of economic variables). The forecast methodology aims at addressing these challenges. The algorithm is said to be "adaptive" insofar as it adapts to the quantity of structural change it detects in the economy by giving more weight to more recent observations. The performance of the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six major economies (USA, UK, Germany, France, Japan, Italy). The performance of Adaptive Trees is on average broadly similar to forecasts obtained from the OECD's Indicator Model and generally performs better than a simple AR(1) benchmark model as well as Random Forests and Gradient Boosted Trees. |
| Beschreibung: | 1 Online-Ressource (43 p.) |
| DOI: | 10.1787/5569a0aa-en |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-13-SOC-061276626 | ||
| 003 | DE-627-1 | ||
| 005 | 20231204121035.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210204s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1787/5569a0aa-en |2 doi | |
| 035 | |a (DE-627-1)061276626 | ||
| 035 | |a (DE-599)KEP061276626 | ||
| 035 | |a (FR-PaOEC)5569a0aa-en | ||
| 035 | |a (EBP)061276626 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a C45 |2 jelc | ||
| 084 | |a C53 |2 jelc | ||
| 084 | |a C63 |2 jelc | ||
| 084 | |a C01 |2 jelc | ||
| 084 | |a C18 |2 jelc | ||
| 084 | |a C23 |2 jelc | ||
| 084 | |a E37 |2 jelc | ||
| 100 | 1 | |a Woloszko, Nicolas |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Adaptive Trees: a new approach to economic forecasting |c Nicolas, Woloszko |
| 264 | 1 | |a Paris |b OECD Publishing |c 2020 | |
| 300 | |a 1 Online-Ressource (43 p.) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a OECD Economics Department Working Papers |v no.1593 | |
| 520 | |a The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the continuous change in the distribution of economic variables). The forecast methodology aims at addressing these challenges. The algorithm is said to be "adaptive" insofar as it adapts to the quantity of structural change it detects in the economy by giving more weight to more recent observations. The performance of the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six major economies (USA, UK, Germany, France, Japan, Italy). The performance of Adaptive Trees is on average broadly similar to forecasts obtained from the OECD's Indicator Model and generally performs better than a simple AR(1) benchmark model as well as Random Forests and Gradient Boosted Trees. | ||
| 650 | 4 | |a Economics | |
| 856 | 4 | 0 | |l FWS01 |p ZDB-13-SOC |q FWS_PDA_SOC |u https://doi.org/10.1787/5569a0aa-en |3 Volltext |
| 912 | |a ZDB-13-SOC | ||
| 912 | |a ZDB-13-SOC | ||
| 951 | |a BO | ||
| 912 | |a ZDB-13-SOC | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-13-SOC-061276626 |
|---|---|
| _version_ | 1816797339012038656 |
| adam_text | |
| any_adam_object | |
| author | Woloszko, Nicolas |
| author_facet | Woloszko, Nicolas |
| author_role | aut |
| author_sort | Woloszko, Nicolas |
| author_variant | n w nw |
| building | Verbundindex |
| bvnumber | localFWS |
| collection | ZDB-13-SOC |
| ctrlnum | (DE-627-1)061276626 (DE-599)KEP061276626 (FR-PaOEC)5569a0aa-en (EBP)061276626 |
| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1787/5569a0aa-en |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02229cam a22004212 4500</leader><controlfield tag="001">ZDB-13-SOC-061276626</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20231204121035.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210204s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1787/5569a0aa-en</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)061276626</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP061276626</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(FR-PaOEC)5569a0aa-en</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)061276626</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="084" ind1=" " ind2=" "><subfield code="a">C45</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C53</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C63</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C01</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C18</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C23</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">E37</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Woloszko, Nicolas</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive Trees: a new approach to economic forecasting</subfield><subfield code="c">Nicolas, Woloszko</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Paris</subfield><subfield code="b">OECD Publishing</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (43 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="490" ind1="0" ind2=" "><subfield code="a">OECD Economics Department Working Papers</subfield><subfield code="v">no.1593</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the continuous change in the distribution of economic variables). The forecast methodology aims at addressing these challenges. The algorithm is said to be "adaptive" insofar as it adapts to the quantity of structural change it detects in the economy by giving more weight to more recent observations. The performance of the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six major economies (USA, UK, Germany, France, Japan, Italy). The performance of Adaptive Trees is on average broadly similar to forecasts obtained from the OECD's Indicator Model and generally performs better than a simple AR(1) benchmark model as well as Random Forests and Gradient Boosted Trees.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Economics</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/10.1787/5569a0aa-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</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</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-061276626 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:56:00Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS |
| owner_facet | DE-863 DE-BY-FWS |
| physical | 1 Online-Ressource (43 p.) |
| psigel | ZDB-13-SOC |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | OECD Publishing |
| record_format | marc |
| series2 | OECD Economics Department Working Papers |
| spelling | Woloszko, Nicolas VerfasserIn aut Adaptive Trees: a new approach to economic forecasting Nicolas, Woloszko Paris OECD Publishing 2020 1 Online-Ressource (43 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OECD Economics Department Working Papers no.1593 The present paper develops Adaptive Trees, a new machine learning approach specifically designed for economic forecasting. Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the continuous change in the distribution of economic variables). The forecast methodology aims at addressing these challenges. The algorithm is said to be "adaptive" insofar as it adapts to the quantity of structural change it detects in the economy by giving more weight to more recent observations. The performance of the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six major economies (USA, UK, Germany, France, Japan, Italy). The performance of Adaptive Trees is on average broadly similar to forecasts obtained from the OECD's Indicator Model and generally performs better than a simple AR(1) benchmark model as well as Random Forests and Gradient Boosted Trees. Economics FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/5569a0aa-en Volltext |
| spellingShingle | Woloszko, Nicolas Adaptive Trees: a new approach to economic forecasting Economics |
| title | Adaptive Trees: a new approach to economic forecasting |
| title_auth | Adaptive Trees: a new approach to economic forecasting |
| title_exact_search | Adaptive Trees: a new approach to economic forecasting |
| title_full | Adaptive Trees: a new approach to economic forecasting Nicolas, Woloszko |
| title_fullStr | Adaptive Trees: a new approach to economic forecasting Nicolas, Woloszko |
| title_full_unstemmed | Adaptive Trees: a new approach to economic forecasting Nicolas, Woloszko |
| title_short | Adaptive Trees: a new approach to economic forecasting |
| title_sort | adaptive trees a new approach to economic forecasting |
| topic | Economics |
| topic_facet | Economics |
| url | https://doi.org/10.1787/5569a0aa-en |
| work_keys_str_mv | AT woloszkonicolas adaptivetreesanewapproachtoeconomicforecasting |