Optimal model averaging for divergent-dimensional Poisson regressions

Gespeichert in:

Autor*in:	Zou, Jiahui [verfasserIn] Wang, Wendun - 1984- [verfasserIn] Zhang, Xinyu [verfasserIn] Zou, Guohua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Asymptotic optimality consistency divergent dimension model averaging Poisson regression

Übergeordnetes Werk:	Enthalten in: Econometric reviews - Philadelphia, Pa. : Taylor & Francis, 1982, 41(2022), 7, Seite 775-805
Übergeordnetes Werk:	volume:41 ; year:2022 ; number:7 ; pages:775-805

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1080/07474938.2022.2047508

Katalog-ID:	1816695858

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912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
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912			\|a GBV_ILN_2122
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982			\|2 26 \|1 00 \|x DE-206 \|b This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.

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allfields	10.1080/07474938.2022.2047508 doi (DE-627)1816695858 (DE-599)KXP1816695858 DE-627 ger DE-627 rda eng Zou, Jiahui verfasserin (DE-588)1269417835 (DE-627)1817947893 aut Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Asymptotic optimality (dpeaa)DE-206 consistency (dpeaa)DE-206 divergent dimension (dpeaa)DE-206 model averaging (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Wang, Wendun 1984- verfasserin (DE-588)14272503X (DE-627)638513626 (DE-576)333041585 aut Zhang, Xinyu verfasserin (DE-588)142263249 (DE-627)634714791 (DE-576)329789376 aut Zou, Guohua verfasserin (DE-588)132658720 (DE-627)524723370 (DE-576)299285359 aut Enthalten in Econometric reviews Philadelphia, Pa. : Taylor & Francis, 1982 41(2022), 7, Seite 775-805 Online-Ressource (DE-627)326061193 (DE-600)2041746-9 (DE-576)116897554 1532-4168 nnns volume:41 year:2022 number:7 pages:775-805 https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 Verlag kostenfrei https://doi.org/10.1080/07474938.2022.2047508 Resolving-System kostenfrei https://www.tandfonline.com/doi/epub/10.1080/07474938.2022.2047508 Verlag kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 41 2022 7 775-805 26 01 0206 418877410X x1z 15-09-22 2403 01 DE-LFER 4195300053 00 --%%-- --%%-- n --%%-- l01 08-10-22 2403 01 DE-LFER https://doi.org/10.1080/07474938.2022.2047508 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 26 00 DE-206 This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
spelling	10.1080/07474938.2022.2047508 doi (DE-627)1816695858 (DE-599)KXP1816695858 DE-627 ger DE-627 rda eng Zou, Jiahui verfasserin (DE-588)1269417835 (DE-627)1817947893 aut Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Asymptotic optimality (dpeaa)DE-206 consistency (dpeaa)DE-206 divergent dimension (dpeaa)DE-206 model averaging (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Wang, Wendun 1984- verfasserin (DE-588)14272503X (DE-627)638513626 (DE-576)333041585 aut Zhang, Xinyu verfasserin (DE-588)142263249 (DE-627)634714791 (DE-576)329789376 aut Zou, Guohua verfasserin (DE-588)132658720 (DE-627)524723370 (DE-576)299285359 aut Enthalten in Econometric reviews Philadelphia, Pa. : Taylor & Francis, 1982 41(2022), 7, Seite 775-805 Online-Ressource (DE-627)326061193 (DE-600)2041746-9 (DE-576)116897554 1532-4168 nnns volume:41 year:2022 number:7 pages:775-805 https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 Verlag kostenfrei https://doi.org/10.1080/07474938.2022.2047508 Resolving-System kostenfrei https://www.tandfonline.com/doi/epub/10.1080/07474938.2022.2047508 Verlag kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 41 2022 7 775-805 26 01 0206 418877410X x1z 15-09-22 2403 01 DE-LFER 4195300053 00 --%%-- --%%-- n --%%-- l01 08-10-22 2403 01 DE-LFER https://doi.org/10.1080/07474938.2022.2047508 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 26 00 DE-206 This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
allfields_unstemmed	10.1080/07474938.2022.2047508 doi (DE-627)1816695858 (DE-599)KXP1816695858 DE-627 ger DE-627 rda eng Zou, Jiahui verfasserin (DE-588)1269417835 (DE-627)1817947893 aut Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Asymptotic optimality (dpeaa)DE-206 consistency (dpeaa)DE-206 divergent dimension (dpeaa)DE-206 model averaging (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Wang, Wendun 1984- verfasserin (DE-588)14272503X (DE-627)638513626 (DE-576)333041585 aut Zhang, Xinyu verfasserin (DE-588)142263249 (DE-627)634714791 (DE-576)329789376 aut Zou, Guohua verfasserin (DE-588)132658720 (DE-627)524723370 (DE-576)299285359 aut Enthalten in Econometric reviews Philadelphia, Pa. : Taylor & Francis, 1982 41(2022), 7, Seite 775-805 Online-Ressource (DE-627)326061193 (DE-600)2041746-9 (DE-576)116897554 1532-4168 nnns volume:41 year:2022 number:7 pages:775-805 https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 Verlag kostenfrei https://doi.org/10.1080/07474938.2022.2047508 Resolving-System kostenfrei https://www.tandfonline.com/doi/epub/10.1080/07474938.2022.2047508 Verlag kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 41 2022 7 775-805 26 01 0206 418877410X x1z 15-09-22 2403 01 DE-LFER 4195300053 00 --%%-- --%%-- n --%%-- l01 08-10-22 2403 01 DE-LFER https://doi.org/10.1080/07474938.2022.2047508 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 26 00 DE-206 This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
allfieldsGer	10.1080/07474938.2022.2047508 doi (DE-627)1816695858 (DE-599)KXP1816695858 DE-627 ger DE-627 rda eng Zou, Jiahui verfasserin (DE-588)1269417835 (DE-627)1817947893 aut Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Asymptotic optimality (dpeaa)DE-206 consistency (dpeaa)DE-206 divergent dimension (dpeaa)DE-206 model averaging (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Wang, Wendun 1984- verfasserin (DE-588)14272503X (DE-627)638513626 (DE-576)333041585 aut Zhang, Xinyu verfasserin (DE-588)142263249 (DE-627)634714791 (DE-576)329789376 aut Zou, Guohua verfasserin (DE-588)132658720 (DE-627)524723370 (DE-576)299285359 aut Enthalten in Econometric reviews Philadelphia, Pa. : Taylor & Francis, 1982 41(2022), 7, Seite 775-805 Online-Ressource (DE-627)326061193 (DE-600)2041746-9 (DE-576)116897554 1532-4168 nnns volume:41 year:2022 number:7 pages:775-805 https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 Verlag kostenfrei https://doi.org/10.1080/07474938.2022.2047508 Resolving-System kostenfrei https://www.tandfonline.com/doi/epub/10.1080/07474938.2022.2047508 Verlag kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 41 2022 7 775-805 26 01 0206 418877410X x1z 15-09-22 2403 01 DE-LFER 4195300053 00 --%%-- --%%-- n --%%-- l01 08-10-22 2403 01 DE-LFER https://doi.org/10.1080/07474938.2022.2047508 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 26 00 DE-206 This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
allfieldsSound	10.1080/07474938.2022.2047508 doi (DE-627)1816695858 (DE-599)KXP1816695858 DE-627 ger DE-627 rda eng Zou, Jiahui verfasserin (DE-588)1269417835 (DE-627)1817947893 aut Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Asymptotic optimality (dpeaa)DE-206 consistency (dpeaa)DE-206 divergent dimension (dpeaa)DE-206 model averaging (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Wang, Wendun 1984- verfasserin (DE-588)14272503X (DE-627)638513626 (DE-576)333041585 aut Zhang, Xinyu verfasserin (DE-588)142263249 (DE-627)634714791 (DE-576)329789376 aut Zou, Guohua verfasserin (DE-588)132658720 (DE-627)524723370 (DE-576)299285359 aut Enthalten in Econometric reviews Philadelphia, Pa. : Taylor & Francis, 1982 41(2022), 7, Seite 775-805 Online-Ressource (DE-627)326061193 (DE-600)2041746-9 (DE-576)116897554 1532-4168 nnns volume:41 year:2022 number:7 pages:775-805 https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 Verlag kostenfrei https://doi.org/10.1080/07474938.2022.2047508 Resolving-System kostenfrei https://www.tandfonline.com/doi/epub/10.1080/07474938.2022.2047508 Verlag kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 41 2022 7 775-805 26 01 0206 418877410X x1z 15-09-22 2403 01 DE-LFER 4195300053 00 --%%-- --%%-- n --%%-- l01 08-10-22 2403 01 DE-LFER https://doi.org/10.1080/07474938.2022.2047508 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/07474938.2022.2047508 26 00 DE-206 This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents.
language	English
source	Enthalten in Econometric reviews 41(2022), 7, Seite 775-805 volume:41 year:2022 number:7 pages:775-805
sourceStr	Enthalten in Econometric reviews 41(2022), 7, Seite 775-805 volume:41 year:2022 number:7 pages:775-805
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institution	findex.gbv.de
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topic_facet	Asymptotic optimality consistency divergent dimension model averaging Poisson regression
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container_title	Econometric reviews
authorswithroles_txt_mv	Zou, Jiahui @@aut@@ Wang, Wendun @@aut@@ Zhang, Xinyu @@aut@@ Zou, Guohua @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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topic_title	26 00 DE-206 This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. We derive an unbiased estimator of the Kullback-Leibler (KL) divergence to choose averaging weights. We show that when all candidate models are misspecified, the proposed estimate is asymptotically optimal by achieving the least KL divergence among all possible averaging estimators. In another situation where correct models exist in the model space, our method can produce consistent coefficient estimates. We apply the proposed techniques to study the determinants and predict corporate innovation outcomes measured by the number of patents Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou Asymptotic optimality (dpeaa)DE-206 consistency (dpeaa)DE-206 divergent dimension (dpeaa)DE-206 model averaging (dpeaa)DE-206 Poisson regression (dpeaa)DE-206
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title_full	Optimal model averaging for divergent-dimensional Poisson regressions Jiahui Zou, Wendun Wang, Xinyu Zhang, and Guohua Zou
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title_short	Optimal model averaging for divergent-dimensional Poisson regressions
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code="x">DE-206</subfield><subfield code="b">This paper proposes a new model averaging method to address model uncertainty in Poisson regressions, allowing the dimension of covariates to increase with the sample size. 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Optimal model averaging for divergent-dimensional Poisson regressions

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