Identifying Age-Cohort-Time Effects, Their Curvature and Interactions from Polynomials: Examples Related to Sickness Absence
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More about this item
Keywords
AGe cohort-time problem; Identification; Polynomial regression; Interaction; Age-cohort curvature; Panel data; Sickness absence;All these keywords.
JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
- I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
- J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
NEP fields
This paper has been announced in the following NEP Reports:- NEP-DEM-2013-06-09 (Demographic Economics)
- NEP-ECM-2013-06-09 (Econometrics)
- NEP-HEA-2013-06-09 (Health Economics)
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