The effectiveness of imprecise probability forecasts
In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability...
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| Veröffentlicht in: | Journal of forecasting 1993-02, Vol.12 (2), p.139-159 |
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| creator | George Benson, P. Whitcomb, Kathleen M. |
| description | In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method. |
| doi_str_mv | 10.1002/for.3980120207 |
| format | Article |
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We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. 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Forecast</addtitle><description>In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.</description><subject>Accuracy</subject><subject>Decision analysis</subject><subject>Entropy</subject><subject>Expected utility</subject><subject>Expected values</subject><subject>Experiments</subject><subject>Forecasting</subject><subject>Forecasting techniques</subject><subject>Forecasts</subject><subject>Imprecise probabilities</subject><subject>Linear programming</subject><subject>Mathematical models</subject><subject>Performance evaluation</subject><subject>Probability</subject><subject>Probability distribution</subject><subject>Probability forecasting</subject><subject>Secondary probabilities</subject><subject>Sensitivity analysis</subject><subject>Studies</subject><subject>Subjective probability</subject><subject>Test Bias</subject><subject>Uncertainty</subject><issn>0277-6693</issn><issn>1099-131X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1993</creationdate><recordtype>article</recordtype><sourceid>K30</sourceid><sourceid>BENPR</sourceid><sourceid>BHHNA</sourceid><recordid>eNqF0U1vEzEQBmALFYm0cOW8ohJw2TBj73rsIyq0gAqVoIiKi-VsxqrbTTa1N0D-Pa6CQK3UcprL89rzIcRThCkCyFdhSFNlDaAECfRATBCsrVHh2Y6YgCSqtbbqkdjN-QIAyKCciOb0nCsOgbsx_uAl51wNoYqLVeIuZq5WaZj5WezjuKnKB9z5PObH4mHwfeYnf-qe-Hr49vTgXX18cvT-4PVx3TVkqLa-6ZrASNYwomGNwRpCUHpOFmw7n_HcgPaKgg4NYcvWa8PKd61vQRq1J15s3y1dXK05j24Rc8d975c8rLMzykojG4Iin98rNWpQVqsCX94LkTQSWKlsoc9u0YthnZZlYCfRom0J2oL270KowFwfxmBR063q0pBz4uBWKS582jgEd21c2a37d7wSsNvAz9jz5j_aHZ58vpGtt9mYR_71N-vTpdOkqHXfPh25jx--t2--0Fnp8jcvdal1</recordid><startdate>199302</startdate><enddate>199302</enddate><creator>George Benson, P.</creator><creator>Whitcomb, Kathleen M.</creator><general>John Wiley & Sons, Ltd</general><general>Wiley</general><general>Wiley Periodicals Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IBDFT</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8BJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>F~G</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7U3</scope><scope>BHHNA</scope></search><sort><creationdate>199302</creationdate><title>The effectiveness of imprecise probability forecasts</title><author>George Benson, P. ; Whitcomb, Kathleen M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4787-9a4c4fe1798e118e61f9871036d79095dbed806a37f6f4715e9a68e3ac5a50283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Accuracy</topic><topic>Decision analysis</topic><topic>Entropy</topic><topic>Expected utility</topic><topic>Expected values</topic><topic>Experiments</topic><topic>Forecasting</topic><topic>Forecasting techniques</topic><topic>Forecasts</topic><topic>Imprecise probabilities</topic><topic>Linear programming</topic><topic>Mathematical models</topic><topic>Performance evaluation</topic><topic>Probability</topic><topic>Probability distribution</topic><topic>Probability forecasting</topic><topic>Secondary probabilities</topic><topic>Sensitivity analysis</topic><topic>Studies</topic><topic>Subjective probability</topic><topic>Test Bias</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>George Benson, P.</creatorcontrib><creatorcontrib>Whitcomb, Kathleen M.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Periodicals Index Online Segment 27</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - 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Forecast</addtitle><date>1993-02</date><risdate>1993</risdate><volume>12</volume><issue>2</issue><spage>139</spage><epage>159</epage><pages>139-159</pages><issn>0277-6693</issn><eissn>1099-131X</eissn><coden>JOFODV</coden><abstract>In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.</abstract><cop>Chichester</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/for.3980120207</doi><tpages>21</tpages></addata></record> |
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| identifier | ISSN: 0277-6693 |
| ispartof | Journal of forecasting, 1993-02, Vol.12 (2), p.139-159 |
| issn | 0277-6693 1099-131X |
| language | eng |
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| source | Sociological Abstracts; Periodicals Index Online; EBSCOhost Business Source Complete |
| subjects | Accuracy Decision analysis Entropy Expected utility Expected values Experiments Forecasting Forecasting techniques Forecasts Imprecise probabilities Linear programming Mathematical models Performance evaluation Probability Probability distribution Probability forecasting Secondary probabilities Sensitivity analysis Studies Subjective probability Test Bias Uncertainty |
| title | The effectiveness of imprecise probability forecasts |
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