Dynamic panel data models and causality

Applications to labor supply, health and insurance

P.C. Michaud

Research output: ThesisDoctoral ThesisScientific

308 Downloads (Pure)

Abstract

One of the main findings concerns the importance of common persistent factors, or unobserved traits of respondents, in order to study dynamic relationships between two variables of interest using panel data. The ¿hand of the past¿ can reinforce existent causal relationships, or blur their effect, potentially leading the analyst to misleading conclusions. In applications using elderly respondents, one must be aware that life-cycle aspects of their decisions and persistent unobserved traits of respondents reinforce the association between a multitude of socio-economic and health variables within households. In insurance markets, the dynamics introduced by experience rating of insurance premiums potentially mask the existence of moral hazard, a short-term response to changes in insurance coverage. In the different applications considered, it is shown how dynamic panel data models can be used to identify causal relationships of interest.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Tilburg University
Supervisors/Advisors
  • van Ours, Jan, Promotor
  • van Soest, Arthur, Promotor
Award date20 May 2005
Place of PublicationTilburg
Publisher
Print ISBNs9056681427
Publication statusPublished - 2005

Fingerprint

Dynamic panel data model
Causality
Labor supply
Insurance
Health
Socio-economics
Insurance market
Experience rating
Analysts
Household
Life cycle
Common factors
Moral hazard
Panel data
Insurance premium

Cite this

Michaud, P. C. (2005). Dynamic panel data models and causality: Applications to labor supply, health and insurance. Tilburg: CentER, Center for Economic Research.
Michaud, P.C.. / Dynamic panel data models and causality : Applications to labor supply, health and insurance. Tilburg : CentER, Center for Economic Research, 2005. 182 p.
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abstract = "One of the main findings concerns the importance of common persistent factors, or unobserved traits of respondents, in order to study dynamic relationships between two variables of interest using panel data. The ¿hand of the past¿ can reinforce existent causal relationships, or blur their effect, potentially leading the analyst to misleading conclusions. In applications using elderly respondents, one must be aware that life-cycle aspects of their decisions and persistent unobserved traits of respondents reinforce the association between a multitude of socio-economic and health variables within households. In insurance markets, the dynamics introduced by experience rating of insurance premiums potentially mask the existence of moral hazard, a short-term response to changes in insurance coverage. In the different applications considered, it is shown how dynamic panel data models can be used to identify causal relationships of interest.",
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year = "2005",
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Michaud, PC 2005, 'Dynamic panel data models and causality: Applications to labor supply, health and insurance', Doctor of Philosophy, Tilburg University, Tilburg.

Dynamic panel data models and causality : Applications to labor supply, health and insurance. / Michaud, P.C.

Tilburg : CentER, Center for Economic Research, 2005. 182 p.

Research output: ThesisDoctoral ThesisScientific

TY - THES

T1 - Dynamic panel data models and causality

T2 - Applications to labor supply, health and insurance

AU - Michaud, P.C.

PY - 2005

Y1 - 2005

N2 - One of the main findings concerns the importance of common persistent factors, or unobserved traits of respondents, in order to study dynamic relationships between two variables of interest using panel data. The ¿hand of the past¿ can reinforce existent causal relationships, or blur their effect, potentially leading the analyst to misleading conclusions. In applications using elderly respondents, one must be aware that life-cycle aspects of their decisions and persistent unobserved traits of respondents reinforce the association between a multitude of socio-economic and health variables within households. In insurance markets, the dynamics introduced by experience rating of insurance premiums potentially mask the existence of moral hazard, a short-term response to changes in insurance coverage. In the different applications considered, it is shown how dynamic panel data models can be used to identify causal relationships of interest.

AB - One of the main findings concerns the importance of common persistent factors, or unobserved traits of respondents, in order to study dynamic relationships between two variables of interest using panel data. The ¿hand of the past¿ can reinforce existent causal relationships, or blur their effect, potentially leading the analyst to misleading conclusions. In applications using elderly respondents, one must be aware that life-cycle aspects of their decisions and persistent unobserved traits of respondents reinforce the association between a multitude of socio-economic and health variables within households. In insurance markets, the dynamics introduced by experience rating of insurance premiums potentially mask the existence of moral hazard, a short-term response to changes in insurance coverage. In the different applications considered, it is shown how dynamic panel data models can be used to identify causal relationships of interest.

M3 - Doctoral Thesis

SN - 9056681427

T3 - CentER Dissertation Series

PB - CentER, Center for Economic Research

CY - Tilburg

ER -

Michaud PC. Dynamic panel data models and causality: Applications to labor supply, health and insurance. Tilburg: CentER, Center for Economic Research, 2005. 182 p. (CentER Dissertation Series).