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 Artikel |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2008
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| Schlagworte: | |
| Online-Zugang: | 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 p.) 16 x 23cm. |
| DOI: | 10.1787/jbcma-v2007-art15-en |
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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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| spelling | Biau, Gérard VerfasserIn 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 p.) 16 x 23cm. Text txt rdacontent Computermedien c rdamedia Online-Ressource 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 MitwirkendeR ctb Rouvière, Laurent MitwirkendeR ctb Enthalten in Journal of Business Cycle Measurement and Analysis Vol. 2007, no. 3, p. 317-331 volume:2007 year:2007 number:3 pages:317-331 FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/jbcma-v2007-art15-en 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_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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