Specification and inference in linear models

In the framework of I.I.D. sampling, a general class of linear models is analyzed. Incidental parameters are shown to naturally arise in this class of models. More fundamentally, special attention is paid to the high dimensionality of the parameter space. The objective of the paper is to offer a strategy for progressively specifying a model within that class of linear models. By so doing, we aim at displaying the precise role of each assumption, at offering alternatives to unnecessarily restrictive specifications, and, thereby, at improving the robustness of the inference procedures we discuss. Decompositions of the inference process are obtained through a systematic use of (Bayesian) cuts. Maximum Likelihood Estimation and Bayesian Inference are discussed.An objective of the progressive specification is to preserve the computational tractability and the interpretability of the procedures we develop by relying on known properties of the usual multivariate regression model.     

Perspektive für junge Pflegebedürftige

Gro?er Bedarf – wenig Angebot – Station?re Intensiv- und Schwerstpflegepl?tze für junge Menschen sind selten zu finden in deutschen Pflegeeinrichtungen. Dabei sind Bedarf und Nachfrage gro?. Das Berliner Sanatorium West hat sich darauf eingestellt und einen Wohnbereich für junge Menschen ab 20 Jahre eingerichtet.     

Entry and Competition in the Postal Market: Foundations for the Construction of Entry Scenarios*

This paper presents a model of entry and imperfect competition, which is inspired by the product differentiation literature and incorporates facts pertaining to the postal sector. There are two operators: incumbent and potential entrant. The entrant offers only one of the products (commercial mail) with a specific technology and delivers only to part of the addressees (located in low cost areas). Its degree of coverage is viewed as a quality attribute; it affects demand and hence market share. The incumbent faces a USO while the entrant is an unregulated profit maximizing firm. To illustrate the potential applications of our approach, we provide some numerical simulations of entry scenarios.     

Regularization of nonparametric frontier estimators

In production theory and efficiency analysis, we estimate the production frontier, the locus of the maximal attainable level of an output (the production), given a set of inputs (the production factors). In other setups, we estimate rather an input (or cost) frontier, the minimal level of the input (cost) attainable for a given set of outputs (goods or services produced). In both cases the problem can be viewed as estimating a surface under shape constraints (monotonicity, …). In this paper we derive the theory of an estimator of the frontier having an asymptotic normal distribution. It is based on the order-m$m$ partial frontier where we let the order m$m$ to converge to infinity when n→∞$n\to \infty$ but at a slow rate. The final estimator is then corrected for its inherent bias. We thus can view our estimator as a regularized frontier. In addition, the estimator is more robust to extreme values and outliers than the usual nonparametric frontier estimators, like FDH and than the unregularized order-_mn$m_n$ estimator of Cazals et al. (2002) converging to the frontier with a Weibull distribution if _mn→∞$m_n\to \infty$ fast enough when n→∞$n\to \infty$. The performances of our estimators are evaluated in finite samples and compared to other estimators through some Monte-Carlo experiments, showing a better behavior (in terms of robustness, bias, MSE and achieved coverage of the resulting confidence intervals). The practical implementation and the robustness properties are illustrated through simulated data sets but also with a real data set.     

Iterative algorithm for non parametric estimation of the instrumental variables quantiles

This paper proposes a simple algorithm for the numerical computation of the non parametric IV quantile estimation. This algorithm is based on the Landweber iterations for solving a nonlinear integral equation. The paper is illustrated by numerical simulations.     

Sport statt Sturz

Pr?vention ist ein Muss - Schmerzhafte H?matome, Frakturen, schwerste Kopfverletzungen k?nnen – neben Folgesch?den – auch zu erheblichen Therapiekosten führen. Deshalb geh?ren Ma?nahmen zur Sturzpr?vention in den Pflichtenkatalog von Pflegeeinrichtungen. Im Berliner Sanatorium West werden Risiken erfasst und Ma?nahmen abgeleitet.     

Der deutsche Strommarkt im Umbruch: Zur Notwendigkeit einer Marktordnung aus einem Guss

The German electricity market is changing fundamentally as renewable energy replaces conventional sources. Simultaneously, regional imbalances between generation and consumption are appearing. The authors sketch a new market design for the German electricity market, jointly taking into account the efficient expansion of renewables and the grid’s stability. A premium paid in addition to the spot market price promotes renewable electricity. Furthermore, they propose to split the German electricity market into a small number of price zones to cope with insufficient transmission capacities. By systematically strengthening regional and temporal price signals, the authors expect a notable cost reduction of the energy transition to be achieved.     

Nonparametric estimation of an instrumental regression: A quasi-Bayesian approach based on regularized posterior

We propose a quasi-Bayesian nonparametric approach to estimating the structural relationship φ$\phi$ among endogenous variables when instruments are available. We show that the posterior distribution of φ$\phi$ is inconsistent in the frequentist sense. We interpret this fact as the ill-posedness of the Bayesian inverse problem defined by the relation that characterizes the structural function φ$\phi$. To solve this problem, we construct a regularized posterior distribution, based on a Tikhonov regularization of the inverse of the marginal variance of the sample, which is justified by a penalized projection argument. This regularized posterior distribution is consistent in the frequentist sense and its mean can be interpreted as the mean of the exact posterior distribution resulting from a Gaussian prior distribution with a shrinking covariance operator.     

Verteilungswirkungen des Erneuerbare-Energien-Gesetzes

The ongoing economic criticism of the German Renewable Energy Act (EEG) recently started focusing on distributional effects, too. A team of authors at the Cologne Institute for Economic Research is worried about high costs for electricity consumers. They argue since electric power consumption is only slightly correlated with income, the fi nancial burden of the EEG is substantially higher for low-income households than for high-income households. This regressive effect is even increased since particularly high-income households have installed photovoltaic systems and thereby gain from green power subsidies. In contrast, other authors argue that this debate on distributional effects, while desirable, often scandalizes rather common issues of everyday economic events while ignoring the real EEG-induced redistribution effects, that is to say the EEG reallocation charge privilege for energy-intensive industries. These authors advise against drawing on partial analysis when discussing distribution-related issues of the EEG. A team of authors at the ZEW are concerned about the cost-effi ciency of the EEG which is the underlying reason for the recent discussions on distributional effects. The energy transition will entail large additional cost which will have to be carried. Increasing the effi ciency of governmental energy and climate policies will increase acceptance and attenuate distributional effects.     

Efficient estimation of general dynamic models with a continuum of moment conditions

There are two difficulties with the implementation of the characteristic function-based estimators. First, the optimal instrument yielding the ML efficiency depends on the unknown probability density function. Second, the need to use a large set of moment conditions leads to the singularity of the covariance matrix. We resolve the two problems in the framework of GMM with a continuum of moment conditions. A new optimal instrument relies on the double indexing and, as a result, has a simple exponential form. The singularity problem is addressed via a penalization term. We introduce HAC-type estimators for non-Markov models. A simulated method of moments is proposed for non-analytical cases.