Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data:
A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Surveys (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, whi...
Gespeichert in:
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| Weitere Verfasser: | , |
| Format: | Elektronisch Buchkapitel |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2008
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| Schlagworte: | |
| Online-Zugang: | DE-384 DE-473 DE-824 DE-29 DE-739 DE-355 DE-20 DE-1028 DE-1049 DE-521 DE-861 DE-898 DE-92 DE-91 DE-573 DE-19 Volltext |
| Zusammenfassung: | A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Surveys (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, which are currently used in short term analysis and considered by forecasters as explanatory variables in many models. In the present paper, we discuss a new statistical approach to forecast the manufacturing growth from firm-survey responses. We base our predictions on a forecasting algorithm inspired by the random forest regression method, which is known to enjoy good prediction properties. Our algorithm exploits the heterogeneity of the survey responses, works fast, is robust to noise and allows for the treatment of missing values. Starting from a real application on a French dataset related to the manufacturing sector, this procedure appears as a competitive method compared with traditional algorithms |
| Beschreibung: | 1 Online-Ressource (15 Seiten) 16 x 23cm |
| DOI: | 10.1787/jbcma-v2007-art15-en |
Internformat
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| 520 | |a A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Surveys (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, which are currently used in short term analysis and considered by forecasters as explanatory variables in many models. In the present paper, we discuss a new statistical approach to forecast the manufacturing growth from firm-survey responses. We base our predictions on a forecasting algorithm inspired by the random forest regression method, which is known to enjoy good prediction properties. Our algorithm exploits the heterogeneity of the survey responses, works fast, is robust to noise and allows for the treatment of missing values. Starting from a real application on a French dataset related to the manufacturing sector, this procedure appears as a competitive method compared with traditional algorithms | ||
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Datensatz im Suchindex
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| spelling | Biau, Gérard Verfasser aut Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data Gérard Biau, Olivier Biau and Laurent Rouvière Paris OECD Publishing 2008 1 Online-Ressource (15 Seiten) 16 x 23cm txt rdacontent c rdamedia cr rdacarrier A large majority of summary indicators derived from the individual responses to qualitative Business Tendency Surveys (which are mostly three-modality questions) result from standard aggregation and quantification methods. This is typically the case for the indicators called balances of opinion, which are currently used in short term analysis and considered by forecasters as explanatory variables in many models. In the present paper, we discuss a new statistical approach to forecast the manufacturing growth from firm-survey responses. We base our predictions on a forecasting algorithm inspired by the random forest regression method, which is known to enjoy good prediction properties. Our algorithm exploits the heterogeneity of the survey responses, works fast, is robust to noise and allows for the treatment of missing values. Starting from a real application on a French dataset related to the manufacturing sector, this procedure appears as a competitive method compared with traditional algorithms Economics Biau, Olivier ctb Rouvière, Laurent ctb https://doi.org/10.1787/jbcma-v2007-art15-en Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Biau, Gérard Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data Economics |
| title | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data |
| title_auth | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data |
| title_exact_search | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data |
| title_exact_search_txtP | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data |
| title_full | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data Gérard Biau, Olivier Biau and Laurent Rouvière |
| title_fullStr | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data Gérard Biau, Olivier Biau and Laurent Rouvière |
| title_full_unstemmed | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data Gérard Biau, Olivier Biau and Laurent Rouvière |
| title_short | Nonparametric Forecasting of the Manufacturing Output Growth with Firm-level Survey Data |
| title_sort | nonparametric forecasting of the manufacturing output growth with firm level survey data |
| topic | Economics |
| topic_facet | Economics |
| url | https://doi.org/10.1787/jbcma-v2007-art15-en |
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