Arbeitspapier
Age-cohort-time effects in sickness absence: Exploring a large data set by polynomial regression
Identification of equations explaining a continuous variable, e.g., the length of sickness absence spells, by age, cohort and time (ACT), subject to their definitional identity is reconsidered. Various extensions of a linear equation to polynomials are explored. If no interactions between the ACT variables are included, only the coefficients of the linear terms create identification problems. A data set with 4.5 million individual observations for long-term sickness absence in Norway is used. The sensitivity of the estimated marginal effects of cohort and age on the length of the absence spells, at the sample mean, is illustrated. Notable differences are found between linear and quadratic equations on the one hand and cubic and fourth-order polynomials on the other. Representing heterogeneity by cohort effects is compared with representing heterogeneity by random and fixed individual effects. On the whole, the age coefficients in the estimated regressions are quite sensitive to how heterogeneity is modeled.
- Sprache
-
Englisch
- Erschienen in
-
Series: Memorandum ; No. 19/2013
Klassifikation
Wirtschaft
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Model Evaluation, Validation, and Selection
Social Security and Public Pensions
Health: Government Policy; Regulation; Public Health
Labor Force and Employment, Size, and Structure
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Model Evaluation, Validation, and Selection
Social Security and Public Pensions
Health: Government Policy; Regulation; Public Health
Labor Force and Employment, Size, and Structure
Thema
Age-cohort-time problem
identification
sickness absence
sickness and gender
panel data
polynomial regression
interaction
heterogeneity
identification
sickness absence
sickness and gender
panel data
polynomial regression
interaction
heterogeneity
Ereignis
Geistige Schöpfung
(wer)
Biørn, Erik
Ereignis
Veröffentlichung
(wer)
University of Oslo, Department of Economics
(wo)
Oslo
(wann)
2013
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:21 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Biørn, Erik
- University of Oslo, Department of Economics
Entstanden
- 2013
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Random coefficients in regression equation systems: The case with unbalanced panel data
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