Investigating health-related time use with partially observed data
Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are obser...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mullahy, John [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Review of Economics of the Household - Springer US, 2003, 20(2021), 1 vom: 08. Juli, Seite 103-121 |
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| Übergeordnetes Werk: |
volume:20 ; year:2021 ; number:1 ; day:08 ; month:07 ; pages:103-121 |
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| DOI / URN: |
10.1007/s11150-021-09570-x |
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OLC2077949791 |
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10.1007/s11150-021-09570-x doi (DE-627)OLC2077949791 (DE-He213)s11150-021-09570-x-p DE-627 ger DE-627 rakwb eng 330 VZ Mullahy, John verfasserin (orcid)0000-0001-8605-3899 aut Investigating health-related time use with partially observed data 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. Time use Healthy time Partial identification Partial observability Enthalten in Review of Economics of the Household Springer US, 2003 20(2021), 1 vom: 08. Juli, Seite 103-121 (DE-627)363769102 (DE-600)2108192-X (DE-576)266542344 1569-5239 nnns volume:20 year:2021 number:1 day:08 month:07 pages:103-121 https://doi.org/10.1007/s11150-021-09570-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 20 2021 1 08 07 103-121 |
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10.1007/s11150-021-09570-x doi (DE-627)OLC2077949791 (DE-He213)s11150-021-09570-x-p DE-627 ger DE-627 rakwb eng 330 VZ Mullahy, John verfasserin (orcid)0000-0001-8605-3899 aut Investigating health-related time use with partially observed data 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. Time use Healthy time Partial identification Partial observability Enthalten in Review of Economics of the Household Springer US, 2003 20(2021), 1 vom: 08. Juli, Seite 103-121 (DE-627)363769102 (DE-600)2108192-X (DE-576)266542344 1569-5239 nnns volume:20 year:2021 number:1 day:08 month:07 pages:103-121 https://doi.org/10.1007/s11150-021-09570-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 20 2021 1 08 07 103-121 |
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10.1007/s11150-021-09570-x doi (DE-627)OLC2077949791 (DE-He213)s11150-021-09570-x-p DE-627 ger DE-627 rakwb eng 330 VZ Mullahy, John verfasserin (orcid)0000-0001-8605-3899 aut Investigating health-related time use with partially observed data 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. Time use Healthy time Partial identification Partial observability Enthalten in Review of Economics of the Household Springer US, 2003 20(2021), 1 vom: 08. Juli, Seite 103-121 (DE-627)363769102 (DE-600)2108192-X (DE-576)266542344 1569-5239 nnns volume:20 year:2021 number:1 day:08 month:07 pages:103-121 https://doi.org/10.1007/s11150-021-09570-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 20 2021 1 08 07 103-121 |
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10.1007/s11150-021-09570-x doi (DE-627)OLC2077949791 (DE-He213)s11150-021-09570-x-p DE-627 ger DE-627 rakwb eng 330 VZ Mullahy, John verfasserin (orcid)0000-0001-8605-3899 aut Investigating health-related time use with partially observed data 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. Time use Healthy time Partial identification Partial observability Enthalten in Review of Economics of the Household Springer US, 2003 20(2021), 1 vom: 08. Juli, Seite 103-121 (DE-627)363769102 (DE-600)2108192-X (DE-576)266542344 1569-5239 nnns volume:20 year:2021 number:1 day:08 month:07 pages:103-121 https://doi.org/10.1007/s11150-021-09570-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 20 2021 1 08 07 103-121 |
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10.1007/s11150-021-09570-x doi (DE-627)OLC2077949791 (DE-He213)s11150-021-09570-x-p DE-627 ger DE-627 rakwb eng 330 VZ Mullahy, John verfasserin (orcid)0000-0001-8605-3899 aut Investigating health-related time use with partially observed data 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. Time use Healthy time Partial identification Partial observability Enthalten in Review of Economics of the Household Springer US, 2003 20(2021), 1 vom: 08. Juli, Seite 103-121 (DE-627)363769102 (DE-600)2108192-X (DE-576)266542344 1569-5239 nnns volume:20 year:2021 number:1 day:08 month:07 pages:103-121 https://doi.org/10.1007/s11150-021-09570-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 20 2021 1 08 07 103-121 |
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Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. One familiar context is where M>1 outcomes’ respective totals across N>1 time periods are observed but where questions of interest involve features—probabilities, moments, etc.—of their unobserved joint distribution at each of the N time periods. For instance, one might wish to understand the distribution of any type of unhealthy day experienced over a month but have access only to the separate monthly totals of physically unhealthy and mentally unhealthy days that are experienced. After demonstrating methods to partially identify such distributions and related parameters under several sampling assumptions, the paper proceeds to derive bounds on partial effects involving exogenous covariates. These results are applied in three empirical exercises. Whether the proposed bounds prove to be sufficiently tight to usefully inform decisionmakers can only be determined in context, although in this paper’s empirical analysis some of the estimated bounds turn out to be perhaps surprisingly tight. Moreover, it is suggested in the paper’s conclusion that the issues considered in this paper may become increasingly salient for analysts as data privacy policies increasingly constrain analyses. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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10.1007/s11150-021-09570-x |
| up_date |
2024-07-03T18:05:36.280Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2077949791</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505205301.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11150-021-09570-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2077949791</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11150-021-09570-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mullahy, John</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8605-3899</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Investigating health-related time use with partially observed data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper suggests analytical strategies for obtaining informative parameter bounds when multivariate health-related time use data are partially observed in a particular yet common manner. 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