A non-linear forecast combination procedure for binary outcomes:
We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating charac...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
München
CESifo
2015
|
| Schriftenreihe: | CESifo working paper
5175 : Category 11, Empirical and theoretical methods |
| Online-Zugang: | Volltext |
| Zusammenfassung: | We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators-the ISM new order diffusion index and the yield curve spread-to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up. |
| Beschreibung: | 1 Online-Ressource (38 S.) graph. Darst. |
| Format: | . - Acrobat Reader |
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| 520 | 1 | |a We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators-the ISM new order diffusion index and the yield curve spread-to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up. | |
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Datensatz im Suchindex
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:12:24Z |
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| language | English |
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| spelling | Lahiri, Kajal 1947- Verfasser (DE-588)13377080X aut A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang München CESifo 2015 1 Online-Ressource (38 S.) graph. Darst. txt rdacontent c rdamedia cr rdacarrier CESifo working paper 5175 : Category 11, Empirical and theoretical methods We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the quintessential linear combination scheme. To illustrate its usefulness, we apply this methodology to optimally aggregate two currently used leading indicators-the ISM new order diffusion index and the yield curve spread-to predict economic recessions in the United States. We also examine the sources of forecasting gains using a counterfactual experimental set up. . - Acrobat Reader Liu, Yang Verfasser (DE-588)1017346283 aut CESifo working paper 5175 : Category 11, Empirical and theoretical methods (DE-604)BV014083264 5175 https://www.cesifo.org/DocDL/cesifo1_wp5175.pdf Verlag kostenfrei Volltext |
| spellingShingle | Lahiri, Kajal 1947- Liu, Yang A non-linear forecast combination procedure for binary outcomes CESifo working paper |
| title | A non-linear forecast combination procedure for binary outcomes |
| title_auth | A non-linear forecast combination procedure for binary outcomes |
| title_exact_search | A non-linear forecast combination procedure for binary outcomes |
| title_full | A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang |
| title_fullStr | A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang |
| title_full_unstemmed | A non-linear forecast combination procedure for binary outcomes Kajal Lahiri ; Liu Yang |
| title_short | A non-linear forecast combination procedure for binary outcomes |
| title_sort | a non linear forecast combination procedure for binary outcomes |
| url | https://www.cesifo.org/DocDL/cesifo1_wp5175.pdf |
| volume_link | (DE-604)BV014083264 |
| work_keys_str_mv | AT lahirikajal anonlinearforecastcombinationprocedureforbinaryoutcomes AT liuyang anonlinearforecastcombinationprocedureforbinaryoutcomes |