Simpler is better: Predicting consumer vehicle purchases in the short run
When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Doremus, Jacqueline [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan - Hering, Alexandra ELSEVIER, 2019, the international journal of the political, economic, planning, environmental and social aspects of energy, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:129 ; year:2019 ; pages:1404-1415 ; extent:12 |
| Links: |
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| DOI / URN: |
10.1016/j.enpol.2019.02.051 |
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| 520 | |a When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. | ||
| 520 | |a When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. | ||
| 650 | 7 | |a Consumer vehicle choice modeling |2 Elsevier | |
| 650 | 7 | |a Validation |2 Elsevier | |
| 650 | 7 | |a Discrete choice modeling |2 Elsevier | |
| 650 | 7 | |a Vehicle demand |2 Elsevier | |
| 700 | 1 | |a Helfand, Gloria |4 oth | |
| 700 | 1 | |a Liu, Changzheng |4 oth | |
| 700 | 1 | |a Donahue, Marie |4 oth | |
| 700 | 1 | |a Kahan, Ari |4 oth | |
| 700 | 1 | |a Shelby, Michael |4 oth | |
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10.1016/j.enpol.2019.02.051 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000894.pica (DE-627)ELV046479619 (ELSEVIER)S0301-4215(19)30134-X DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Doremus, Jacqueline verfasserin aut Simpler is better: Predicting consumer vehicle purchases in the short run 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. Consumer vehicle choice modeling Elsevier Validation Elsevier Discrete choice modeling Elsevier Vehicle demand Elsevier Helfand, Gloria oth Liu, Changzheng oth Donahue, Marie oth Kahan, Ari oth Shelby, Michael oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:129 year:2019 pages:1404-1415 extent:12 https://doi.org/10.1016/j.enpol.2019.02.051 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 129 2019 1404-1415 12 |
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10.1016/j.enpol.2019.02.051 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000894.pica (DE-627)ELV046479619 (ELSEVIER)S0301-4215(19)30134-X DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Doremus, Jacqueline verfasserin aut Simpler is better: Predicting consumer vehicle purchases in the short run 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. Consumer vehicle choice modeling Elsevier Validation Elsevier Discrete choice modeling Elsevier Vehicle demand Elsevier Helfand, Gloria oth Liu, Changzheng oth Donahue, Marie oth Kahan, Ari oth Shelby, Michael oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:129 year:2019 pages:1404-1415 extent:12 https://doi.org/10.1016/j.enpol.2019.02.051 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 129 2019 1404-1415 12 |
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10.1016/j.enpol.2019.02.051 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000894.pica (DE-627)ELV046479619 (ELSEVIER)S0301-4215(19)30134-X DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Doremus, Jacqueline verfasserin aut Simpler is better: Predicting consumer vehicle purchases in the short run 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. Consumer vehicle choice modeling Elsevier Validation Elsevier Discrete choice modeling Elsevier Vehicle demand Elsevier Helfand, Gloria oth Liu, Changzheng oth Donahue, Marie oth Kahan, Ari oth Shelby, Michael oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:129 year:2019 pages:1404-1415 extent:12 https://doi.org/10.1016/j.enpol.2019.02.051 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 129 2019 1404-1415 12 |
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10.1016/j.enpol.2019.02.051 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000894.pica (DE-627)ELV046479619 (ELSEVIER)S0301-4215(19)30134-X DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Doremus, Jacqueline verfasserin aut Simpler is better: Predicting consumer vehicle purchases in the short run 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. Consumer vehicle choice modeling Elsevier Validation Elsevier Discrete choice modeling Elsevier Vehicle demand Elsevier Helfand, Gloria oth Liu, Changzheng oth Donahue, Marie oth Kahan, Ari oth Shelby, Michael oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:129 year:2019 pages:1404-1415 extent:12 https://doi.org/10.1016/j.enpol.2019.02.051 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 129 2019 1404-1415 12 |
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10.1016/j.enpol.2019.02.051 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000894.pica (DE-627)ELV046479619 (ELSEVIER)S0301-4215(19)30134-X DE-627 ger DE-627 rakwb eng 610 VZ 77.50 bkl Doremus, Jacqueline verfasserin aut Simpler is better: Predicting consumer vehicle purchases in the short run 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. Consumer vehicle choice modeling Elsevier Validation Elsevier Discrete choice modeling Elsevier Vehicle demand Elsevier Helfand, Gloria oth Liu, Changzheng oth Donahue, Marie oth Kahan, Ari oth Shelby, Michael oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:129 year:2019 pages:1404-1415 extent:12 https://doi.org/10.1016/j.enpol.2019.02.051 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 129 2019 1404-1415 12 |
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When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. |
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When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. |
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When agencies such as the US Environmental Protection Agency (EPA) establish future greenhouse gas emissions standards for new vehicles, forecasting future vehicle purchases due to changes in fuel economy and prices provides insight into regulatory impacts. We compare predictions from a nested logit model independently developed for US EPA to a simple model where past market share predicts future market share using data from model years 2008, 2010, and 2016. The simple model outperforms the nested logit model for all goodness-of-prediction measures for both prediction years. Including changes in vehicle price and fuel economy increases bias in forecasted market shares. This bias suggests price increases are correlated with unobserved increases in vehicle quality, changes in preferences, or brand-specific changes in market size but not cost pass-through. For 2010, past shares predict better than a nested logit model despite a major shock, the economic disruption caused by the Great Recession. Observed share changes during this turbulent period may offer upper bounds for policy changes in other contexts: the largest observed change in market share across the two horizons is 6.6% for manufacturers in 2016 and 3.4% for an individual vehicle in 2010. |
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