On the Returns to Occupational Qualification in Terms of Subjective and Objective Variables: A GEE-type Approach to the Estimation of Two-Equation Panel Models

This article proposes an estimation approach for panel models with mixed continuous and ordered categorical outcomes based on generalized estimating equations for the mean and pseudo-score equations for the covariance parameters. A numerical study suggests that efficiency can be gained as concerns the mean parameter estimators by using individual covariance matrices in the estimating equations for the mean parameters. The approach is applied to estimate the returns to occupational qualification in terms of income and perceived job security in a nine-year period based on the German Socio-Economic Panel (SOEP). To compensate for missing data, a combined multiple imputation/weighting approach is adopted

A generalized estimating/pseudo-score equations approach for the estimation of structural equation models

The results of two simulation studies suggest a mixed `generalized estimating/pseudo-score equations' approach to lead to more efficient estimators than a GEE approach proposed by Qu, Williams, Beck and Medendorp (1992) or a three-stage approach as proposed e.g. by Schepers, Arminger and Küsters (1991) in panel probit models with binary responses. Furthermore, the mixed approach led to very efficient estimators of regression and correlation structure parameter estimators in an assumed underlying model relative to the ML estimator for an equicorrelation structure. Using the mixed approach, the regression parameters are estimated using generalized estimating equations and the correlation structure parameters are simultaneously estimated using pseudo-score equations. Both sets of parameters are calculated as if they were orthogonal, thereby preserving the robustness of the regression parameter estimators with respect to misspecification of the correlation matrix. Based on the above simulation results, the mixed approach is extended for the estimation of more general structural equation models with ordered categorical or mixed continuous/ordered categorical responses

Derivation of design weights: the case of the German socio-economic panel (GSOEP)

Design-based estimators of totals, means or proportions in finite populations generally are functions of weighted sums. If each element selected into the sample is also observed, then for the calculation of the pi-estimator these weights are just the inverse inclusion probabilities of the elements. However, if e.g. nonresponse or attrition over time occurs, calculation of these weights also includes modeling of nonresponse and/or attrition mechanisms. Since models of these mechanisms are disputable, 'pure' design weights can be the basis for cal- culating alternative weights by a different modeling e.g. of nonresponse and/or attrition mechanisms. In the case of complex sampling schemes, however, it is often not possible to derive the exact inclusion probabilities. In that case, weights may be derived based on approximations and/or simplifying assumptions. In this paper, after describing the selection schemes of the subsamples A, B, C, D and E of the German Socio-Economic Panel (GSOEP), approximate design weights are derived which enable users of the GSOEP to calculate their own weights if desired

Evaluation of a Pseudo-R2 measure for panel probit models

A simulation study designed to evaluate the pseudo-R2 proposed by Spiess and Keller (1999) suggests that this measure represents the goodness-of-fit not only of the systematic part, but also of the assumed correlation structure in binary panel probit models

A Selection Model for Panel Data: The Prospects of Green Party Support

Although sample selection bias is a frequent problem of applied research, there has been no generalization of sample selection models with binary dependent variables of interest to data with temporal error correlations. We suggest a generalized estimating equation approach to panel data selection models, considering binary responses in both equations. We demonstrate the utility of this model by a simulation study and by analyzing highly unbalanced annual panel data taken from the German Socio-Economic Panel Study covering two decades of Green party support.This is a pre-copy-editing, author-produced PDF of an article accepted for publication in "Political Analysis" following peer review. The definitive publisher-authenticated version A Selection Model for Panel Data / Martin Spieß, Martin Kroh. In: Political Analysis 18 (2010), 2, pp. 172-188 is available online at: http://dx.doi.org/10.1093/pan/mpp045

Evaluation of a Pseudo-R2 Measure for Panel Probit Models

A simulation study designed to evaluate the pseudo-R2 T proposed by Spiess and Keller (1999) suggests that this measure represents the goodness- of-fit not only of the systematic part, but also of the assumed correlation structure in binary panel probit models.Goodness-of-fit; Pseudo-R2; Panel probit model; Simulation study

Derivation of Design Weights: The Case of the German Socio-Economic Panel (GSOEP)

Design-based estimators of totals, means or proportions in finite populations generally are functions of weighted sums. If each element selected into the sample is also observed, then for the calculation of the pi-estimator these weights are just the inverse inclusion probabilities of the elements. However, if e.g. nonresponse or attrition over time occurs, calculation of these weights also includes modeling of nonresponse and/or attrition mechanisms. Since models of these mechanisms are disputable, 'pure' design weights can be the basis for calculating alternative weights by a different modeling e.g. of nonresponse and/or attrition mechanisms. In the case of complex sampling schemes, however, it is often not possible to derive the exact inclusion probabilities. In that case, weights may be derived based on approximations and/or simplifying assumptions. In this paper, after describing the selection schemes of the subsamples A, B, C, D and E of the German Socio-Economic Panel (GSOEP), approximate design weights are derived which enable users of the GSOEP to calculate their own weights if desired.Design-based inference; approximate design weights; complex surveys; GSOEP

The Data Quality Concept of Accuracy in the Context of Public Use Data Sets

Like other data quality dimensions, the concept of accuracy is often adopted to characterise a particular data set. However, its common specification basically refers to statistical properties of estimators, which can hardly be proved by means of a single survey at hand. This ambiguity can be resolved by assigning ?accuracy? to survey processes that are known to affect these properties. In this contribution, we consider the sub-process of imputation as one important step in setting up a data set and argue that the so called ?hit-rate? criterion, that is intended to measure the accuracy of a data set by some distance function of ?true? but unobserved and imputed values, is neither required nor desirable. In contrast, the so-called ?inference? criterion allows for valid inferences based on a suitably completed data set under rather general conditions. The underlying theoretical concepts are illustrated by means of a simulation study. It is emphasised that the same principal arguments apply to other survey processes that introduce uncertainty into an edited data set

Combining an ongoing panel with a new cross-sectional sample

In this paper, a weight is derived for the calculation of design based estimators of totals, means and proportions using the ongoing German Socio-Economic Panel (GSOEP) and a new cross-sectional sample. In the first part of the paper, the selection schemes of the subsamples A, B, C and D of the ongoing panel and of the new sample (sample E) are described. Using some similarity assumptions and starting from a general formulation, an optimal weight in the sense of small variances of design-based estimators using both samples is derived. The merits of this approach as well as some disadvantages are discussed

GEE Estimation of a Two-Equation Panel Data Model: An Analysis of Wage Dynamics and the Incidence of Profit-Sharing in West Germany

We propose a generalized estimating equations approach to the analysis of the mean and the covariance structure of a bivariate time series process of panel data with mixed continuous and discrete dependent variables. The approach is used to jointly analyze wage dynamics and the incidence of profit-sharing in West Germany. Our findings reveal a significantly positive conditional correlation of wages and the incidence of profit-sharing. Furthermore, they indicate that permanent unobserved individual ability is comparatively more important in the profit-sharing than in the wage equation and show that shocks have a long-lasting effect on transitory wages but not on the incidence of profit-sharing. Hence, the results support theoretical predictions that selection into profit-sharing is mostly due to unobservable ability and that profit-sharing ties wages more closely to productivity