Ill-posed estimation in high-dimensional models with instrumental variables
This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Breunig, Christoph - 1982- [verfasserIn] Mammen, Enno - 1955- [verfasserIn] Simoni, Anna - 1980- [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
10 August 2020 |
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| Schlagwörter: |
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| Anmerkung: |
Gesehen am 04.12.2020 |
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| Umfang: |
30 |
| Übergeordnetes Werk: |
Enthalten in: Journal of econometrics - Amsterdam [u.a.] : Elsevier, 1973, 219(2020), 1, Seite 171-200 |
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| Übergeordnetes Werk: |
volume:219 ; year:2020 ; number:1 ; pages:171-200 ; extent:30 |
| Links: |
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| DOI / URN: |
10.1016/j.jeconom.2020.04.043 |
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| Katalog-ID: |
1742031498 |
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| 245 | 1 | 0 | |a Ill-posed estimation in high-dimensional models with instrumental variables |c Christoph Breunig, Enno Mammen, Anna Simoni |
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| 520 | |a This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. | ||
| 650 | 4 | |a Central limit theorem | |
| 650 | 4 | |a confidence-intervals | |
| 650 | 4 | |a Desparsification | |
| 650 | 4 | |a Ill-posed estimation problem | |
| 650 | 4 | |a inference | |
| 650 | 4 | |a Instrumental Variables | |
| 650 | 4 | |a Lasso | |
| 650 | 4 | |a Linear model | |
| 650 | 4 | |a regions | |
| 650 | 4 | |a regularization | |
| 650 | 4 | |a selection | |
| 650 | 4 | |a Sparsity | |
| 650 | 4 | |a weak | |
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10.1016/j.jeconom.2020.04.043 doi (DE-627)1742031498 (DE-599)KXP1742031498 (OCoLC)1341382967 DE-627 ger DE-627 rda eng Breunig, Christoph 1982- verfasserin (DE-588)1038564115 (DE-627)766160971 (DE-576)318633884 aut Ill-posed estimation in high-dimensional models with instrumental variables Christoph Breunig, Enno Mammen, Anna Simoni 10 August 2020 30 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 04.12.2020 This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Central limit theorem confidence-intervals Desparsification Ill-posed estimation problem inference Instrumental Variables Lasso Linear model regions regularization selection Sparsity weak Mammen, Enno 1955- verfasserin (DE-588)170668606 (DE-627)060788658 (DE-576)13153159X aut Simoni, Anna 1980- verfasserin (DE-588)1036784576 (DE-627)751156019 (DE-576)386835985 aut Enthalten in Journal of econometrics Amsterdam [u.a.] : Elsevier, 1973 219(2020), 1, Seite 171-200 Online-Ressource (DE-627)253781817 (DE-600)1460617-3 (DE-576)072794437 nnns volume:219 year:2020 number:1 pages:171-200 extent:30 https://doi.org/10.1016/j.jeconom.2020.04.043 Resolving-System Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 219 2020 1 171-200 30 2013 01 DE-16-250 3817283105 00 --%%-- --%%-- --%%-- --%%-- l01 04-12-20 2013 01 DE-16-250 00 s hd2020 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_30 2013 01 DE-16-250 04 p (DE-627)1742030599 Breunig, Christoph 2013 01 DE-16-250 04 k (DE-627)1416822720 Extern 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1494774399 Mammen, Enno 2013 01 DE-16-250 05 k (DE-627)141653461X Institut für Angewandte Mathematik (IAM) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 |
| spelling |
10.1016/j.jeconom.2020.04.043 doi (DE-627)1742031498 (DE-599)KXP1742031498 (OCoLC)1341382967 DE-627 ger DE-627 rda eng Breunig, Christoph 1982- verfasserin (DE-588)1038564115 (DE-627)766160971 (DE-576)318633884 aut Ill-posed estimation in high-dimensional models with instrumental variables Christoph Breunig, Enno Mammen, Anna Simoni 10 August 2020 30 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 04.12.2020 This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Central limit theorem confidence-intervals Desparsification Ill-posed estimation problem inference Instrumental Variables Lasso Linear model regions regularization selection Sparsity weak Mammen, Enno 1955- verfasserin (DE-588)170668606 (DE-627)060788658 (DE-576)13153159X aut Simoni, Anna 1980- verfasserin (DE-588)1036784576 (DE-627)751156019 (DE-576)386835985 aut Enthalten in Journal of econometrics Amsterdam [u.a.] : Elsevier, 1973 219(2020), 1, Seite 171-200 Online-Ressource (DE-627)253781817 (DE-600)1460617-3 (DE-576)072794437 nnns volume:219 year:2020 number:1 pages:171-200 extent:30 https://doi.org/10.1016/j.jeconom.2020.04.043 Resolving-System Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 219 2020 1 171-200 30 2013 01 DE-16-250 3817283105 00 --%%-- --%%-- --%%-- --%%-- l01 04-12-20 2013 01 DE-16-250 00 s hd2020 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_30 2013 01 DE-16-250 04 p (DE-627)1742030599 Breunig, Christoph 2013 01 DE-16-250 04 k (DE-627)1416822720 Extern 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1494774399 Mammen, Enno 2013 01 DE-16-250 05 k (DE-627)141653461X Institut für Angewandte Mathematik (IAM) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 |
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10.1016/j.jeconom.2020.04.043 doi (DE-627)1742031498 (DE-599)KXP1742031498 (OCoLC)1341382967 DE-627 ger DE-627 rda eng Breunig, Christoph 1982- verfasserin (DE-588)1038564115 (DE-627)766160971 (DE-576)318633884 aut Ill-posed estimation in high-dimensional models with instrumental variables Christoph Breunig, Enno Mammen, Anna Simoni 10 August 2020 30 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 04.12.2020 This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Central limit theorem confidence-intervals Desparsification Ill-posed estimation problem inference Instrumental Variables Lasso Linear model regions regularization selection Sparsity weak Mammen, Enno 1955- verfasserin (DE-588)170668606 (DE-627)060788658 (DE-576)13153159X aut Simoni, Anna 1980- verfasserin (DE-588)1036784576 (DE-627)751156019 (DE-576)386835985 aut Enthalten in Journal of econometrics Amsterdam [u.a.] : Elsevier, 1973 219(2020), 1, Seite 171-200 Online-Ressource (DE-627)253781817 (DE-600)1460617-3 (DE-576)072794437 nnns volume:219 year:2020 number:1 pages:171-200 extent:30 https://doi.org/10.1016/j.jeconom.2020.04.043 Resolving-System Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 219 2020 1 171-200 30 2013 01 DE-16-250 3817283105 00 --%%-- --%%-- --%%-- --%%-- l01 04-12-20 2013 01 DE-16-250 00 s hd2020 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_30 2013 01 DE-16-250 04 p (DE-627)1742030599 Breunig, Christoph 2013 01 DE-16-250 04 k (DE-627)1416822720 Extern 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1494774399 Mammen, Enno 2013 01 DE-16-250 05 k (DE-627)141653461X Institut für Angewandte Mathematik (IAM) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 |
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10.1016/j.jeconom.2020.04.043 doi (DE-627)1742031498 (DE-599)KXP1742031498 (OCoLC)1341382967 DE-627 ger DE-627 rda eng Breunig, Christoph 1982- verfasserin (DE-588)1038564115 (DE-627)766160971 (DE-576)318633884 aut Ill-posed estimation in high-dimensional models with instrumental variables Christoph Breunig, Enno Mammen, Anna Simoni 10 August 2020 30 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 04.12.2020 This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Central limit theorem confidence-intervals Desparsification Ill-posed estimation problem inference Instrumental Variables Lasso Linear model regions regularization selection Sparsity weak Mammen, Enno 1955- verfasserin (DE-588)170668606 (DE-627)060788658 (DE-576)13153159X aut Simoni, Anna 1980- verfasserin (DE-588)1036784576 (DE-627)751156019 (DE-576)386835985 aut Enthalten in Journal of econometrics Amsterdam [u.a.] : Elsevier, 1973 219(2020), 1, Seite 171-200 Online-Ressource (DE-627)253781817 (DE-600)1460617-3 (DE-576)072794437 nnns volume:219 year:2020 number:1 pages:171-200 extent:30 https://doi.org/10.1016/j.jeconom.2020.04.043 Resolving-System Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 219 2020 1 171-200 30 2013 01 DE-16-250 3817283105 00 --%%-- --%%-- --%%-- --%%-- l01 04-12-20 2013 01 DE-16-250 00 s hd2020 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_30 2013 01 DE-16-250 04 p (DE-627)1742030599 Breunig, Christoph 2013 01 DE-16-250 04 k (DE-627)1416822720 Extern 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1494774399 Mammen, Enno 2013 01 DE-16-250 05 k (DE-627)141653461X Institut für Angewandte Mathematik (IAM) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 |
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10.1016/j.jeconom.2020.04.043 doi (DE-627)1742031498 (DE-599)KXP1742031498 (OCoLC)1341382967 DE-627 ger DE-627 rda eng Breunig, Christoph 1982- verfasserin (DE-588)1038564115 (DE-627)766160971 (DE-576)318633884 aut Ill-posed estimation in high-dimensional models with instrumental variables Christoph Breunig, Enno Mammen, Anna Simoni 10 August 2020 30 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gesehen am 04.12.2020 This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Central limit theorem confidence-intervals Desparsification Ill-posed estimation problem inference Instrumental Variables Lasso Linear model regions regularization selection Sparsity weak Mammen, Enno 1955- verfasserin (DE-588)170668606 (DE-627)060788658 (DE-576)13153159X aut Simoni, Anna 1980- verfasserin (DE-588)1036784576 (DE-627)751156019 (DE-576)386835985 aut Enthalten in Journal of econometrics Amsterdam [u.a.] : Elsevier, 1973 219(2020), 1, Seite 171-200 Online-Ressource (DE-627)253781817 (DE-600)1460617-3 (DE-576)072794437 nnns volume:219 year:2020 number:1 pages:171-200 extent:30 https://doi.org/10.1016/j.jeconom.2020.04.043 Resolving-System Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 219 2020 1 171-200 30 2013 01 DE-16-250 3817283105 00 --%%-- --%%-- --%%-- --%%-- l01 04-12-20 2013 01 DE-16-250 00 s hd2020 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_3 2013 01 DE-16-250 03 s s_30 2013 01 DE-16-250 04 p (DE-627)1742030599 Breunig, Christoph 2013 01 DE-16-250 04 k (DE-627)1416822720 Extern 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1494774399 Mammen, Enno 2013 01 DE-16-250 05 k (DE-627)141653461X Institut für Angewandte Mathematik (IAM) 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_2 |
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Ill-posed estimation in high-dimensional models with instrumental variables Christoph Breunig, Enno Mammen, Anna Simoni |
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ill-posed estimation in high-dimensional models with instrumental variables |
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Ill-posed estimation in high-dimensional models with instrumental variables |
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This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Gesehen am 04.12.2020 |
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This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Gesehen am 04.12.2020 |
| abstract_unstemmed |
This paper is concerned with inference about low-dimensional components of a high-dimensional parameter vector beta(0) which is identified through instrumental variables. We allow for eigenvalues of the expected outer product of included and excluded covariates, denoted by M, to shrink to zero as the sample size increases. We propose a novel estimator based on desparsification of an instrumental variable Lasso estimator, which is a regularized version of 2SLS with an additional correction term. This estimator converges to beta(0) at a rate depending on the mapping properties of M. Linear combinations of our estimator of beta(0) are shown to be asymptotically normally distributed. Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta(0). In MonteCarlo simulations we analyze the finite sample behavior of our estimator. We apply our method to estimate a logit model of demand for automobiles using real market share data. (C) 2020 Elsevier B.V. All rights reserved. Gesehen am 04.12.2020 |
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Ill-posed estimation in high-dimensional models with instrumental variables |
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https://doi.org/10.1016/j.jeconom.2020.04.043 |
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