Well-Being over the Life Span: Semiparametric Evidence from British and German Longitudinal Data

This paper applies semiparametric regression models using penalized splines to investigate the profile of well-being over the life span. Splines have the advantage that they do not require a priori assumptions about the form of the curve. Using data from the British Household Panel Survey (BHPS) and the German Socio-Economic Panel Study (SOEP), the analysis shows a common, quite similar, age-specific pattern of life satisfaction for both Britain and Germany that can be characterized by three age stages. In the first stage, life satisfaction declines until approximately the fifth life decade. In the second age stage, well-being clearly increases and has a second turning point (maximum) after which well-being decreases in the third age stage. Several reasons for the three-phase pattern are discussed. We point to the fact that neither polynomial functions of the third nor the fourth degree describe the relationship adequately: polynomials locate the minimum and the maximum imprecisely. In addition, our analysis discusses the indistinguishability of age, period, and cohort effects: we propose estimating age-period models that control for cohort effects including substantive variables, such as the life expectancy of the birth cohort, and further observed socioeconomic characteristics in the regression.Subjective well-being, life satisfaction, semiparametric regression, penalized splines, age-period model, age-cohort model

KriMI: A Multiple Imputation Approach for Preserving Spatial Dependencies - Imputation of Regional Price Indices using the Example of Bavaria

Multiple imputation is a method to handle the problem of missing values in a dataset. As it accounts for the uncertainty brought in by the missing data, it is possible to conduct reliable statistical tests after this method has been implemented. Kriging uses neighbourhood effects to predict values of unobserved regions. It can be seen as an imputation technique. The unobserved regions are missing data points, and the kriging predictions are the imputations. Due to the fact of being a single imputation technique, no proper statistical inferences are possible after filling the dataset. If spatially dependent data face the problem of missing data and a proper statistical inference is needed, a modelling of the spatial correlation in the multiple imputation model is needed. Here this is prevailed by implementing kriging in the model used for multiple imputation. We call the resulting method KriMI. The exact problem can be found when looking at regional price levels in Bavaria. The Bavarian State Office for Statistics surveys the prices which are needed to compute the price index only in a few regions. The prices of the unobserved regions are treated as missing data

Well-Being over the Life Span: Semiparametric Evidence from British and German Longitudinal Data

This paper applies semiparametric regression models using penalized splines to investigate the profile of well-being over the life span. Splines have the advantage that they do not require a priori assumptions about the form of the curve. Using data from the British Household Panel Survey (BHPS) and the German Socio-Economic Panel Study (SOEP), the analysis shows a common, quite similar, age-specific pattern of life satisfaction for both Britain and Germany that can be characterized by three age stages. In the first stage, life satisfaction declines until approximately the fifth life decade. In the second age stage, well-being clearly increases and has a second turning point (maximum) after which well-being decreases in the third age stage. Several reasons for the three-phase pattern are discussed. We point to the fact that neither polynomial functions of the third nor the fourth degree describe the relationship adequately: polynomials locate the minimum and the maximum imprecisely. In addition, our analysis discusses the indistinguishability of age, period, and cohort effects: we propose estimating age-period models that control for cohort effects including substantive variables, such as the life expectancy of the birth cohort, and further observed socioeconomic characteristics in the regression.subjective well-being, life satisfaction, semiparametric regression, penalized splines, age-period model, age-cohort model

Well-Being over the Life Span: Semiparametric Evidence from British and German Longitudinal Data

This paper applies semiparametric regression models using penalized splines to investigate the profile of well-being over the life span. Splines have the advantage that they do not require a priori assumptions about the form of the curve. Using data from the British Household Panel Survey (BHPS) and the German Socio-Economic Panel Study (SOEP), the analysis shows a common, quite similar, age-specific pattern of life satisfaction for both Britain and Germany that can be characterized by three age stages. In the first stage, life satisfaction declines until approximately the fifth life decade. In the second age stage, well-being clearly increases and has a second turning point (maximum) after which well-being decreases in the third age stage. Several reasons for the three-phase pattern are discussed. We point to the fact that neither polynomial functions of the third nor the fourth degree describe the relationship adequately: polynomials locate the minimum and the maximum imprecisely. In addition, our analysis discusses the indistinguishability of age, period, and cohort effects: we propose estimating age-period models that control for cohort effects including substantive variables, such as the life expectancy of the birth cohort, and further observed socioeconomic characteristics in the regression.subjective well-being, life satisfaction, semiparametric regression, penalized splines, age-period model, age-cohort model