Suchergebnisse

The Cartesian Integration Method in Stochastic Linear Programming -- Anwendungen der Dualität der Optimierungstheorie auf Nichtlineare Approximationsaufgaben -- Iterative Lösung Linearer Ungleichungssysteme -- Eine Primale Version des Benders'schen Dekompositionsverfahrens und Seine Anwendung in der Gemischt-Ganzzahligen Optimierung -- Schwache Stetigkeit bei Nichtlinearen Kontrollproblemen -- Die Berechnung von Verallgemeinerten Quadraturformeln vom Gausschen Typus, eine Optimierungsaufgabe -- Stetigkeitsfragen bei der Diskretisierung Konvexer Optimierungsprobleme -- Optimale Linienführung Innerhalb eines Korridors — Ein Nichtlineares Optimierungsproblem -- Dualität und Optimale Steuerungen -- Optimale Definite Polynome und Quadraturformeln -- Some Numerical Techniques for Optimal Control Governed by Partial Differential Equation.

Multilevel models are widely used in education and social science research. However, the effects of omitting levels of the hierarchy on the variance decomposition and the clustering effects have not been well documented. This paper discusses how omitting one level in three-level models affects the variance decomposition and clustering in the resulting two-level models. Specifically, I used the ANOVA framework and provided results for simple models that do not include predictors and assumed balanced nested data (or designs). The results are useful for teacher and school effects research as well as for power analysis during the designing stage of a study. The usefulness of the methods is demonstrated using data from Project STAR. -- Variance decomposition ; nested designs ; clustering

This paper uses data from the German Socio-Economic Panel for the years 2000 to 2005 to study the earnings differential between self- and dependent employed German men. Constructing a counterfactual earnings distribution for the self-employed in dependent employment and using quantile regression decompositions we find that the earnings differential over the distribution cannot be explained by differences in endowments. Furthermore, low-earning self-employed could earn more in dependent employment. Finally, the observed earnings advantage for the self-employed at the top of the earnings distribution is not associated with higher returns to observable variables.

New Keynesian Phillips Curves (NKPC) have been extensively used in the analysis of monetary policy, but yet there are a number of issues of concern about how they are estimated and then related to the underlying macroeconomic theory. The first is whether such equations are identified. To check identification requires specifying the process for the forcing variables (typically the output gap) and solving the model for inflation in terms of the observables. In practice, the equation is estimated by GMM, relying on statistical criteria to choose instruments. This may result in failure of identification or weak instruments. Secondly, the NKPC is usually derived as a part of a DSGE model, solved by log-linearising around a steady state and the variables are then measured in terms of deviations from the steady state. In practice the steady states, e.g. for output, are usually estimated by some statistical procedure such as the Hodrick-Prescott (HP) filter that might not be appropriate. Thirdly, there are arguments that other variables, e.g. interest rates, foreign inflation and foreign output gaps should enter the Phillips curve. This paper examines these three issues and argues that all three benefit from a global perspective. The global perspective provides additional instruments to alleviate the weak instrument problem, yields a theoretically consistent measure of the steady state and provides a natural route for foreign inflation or output gap to enter the NKPC. -- Global VAR (GVAR) ; identification ; New Keynesian Phillips Curve ; trend-cycle decomposition

This study aimed to identify the major factors underlying the large discrepancy in poverty levels between two Brazilian racial groups: whites and Afro-Brazilians. We performed an Oaxaca-Blinder-type decomposition for nonlinear regressions in order to quantify the extent to which differences in observed geographic, sociodemographic, and labor characteristics (characteristics effect) account for this difference. The remaining unexplained part (coefficients effect) provides evidence on how these characteristics differentially impact on the risk of poverty in each group. A detailed decomposition of both effects allows the individual contribution of each characteristic to be determined. Our results show that the characteristics effect explains a large part of the discrepancy in poverty levels, with education and labor variables of household members explaining at least one half of the effect, and geographic and demographic variables accounting for the remainder. However, the unexplained part that remains significant has increased in importance in recent last years, and probably results from unequal access to high-quality education and the persistence of discrimination against colored workers in the labor market.