Probabilistic Bounds on the Virtual Multipliers in Data Envelopment Analysis: Polyhedral Cone Constraints

The paper is concerned with the incorporation of polyhedral cone constraints on the virtual multipliers in DEA. The incorporation of probabilistic bounds on the virtual multipliers based upon a stochastic benchmark vector is demonstrated. The suggested approach involves a stochastic (chance constrained) programming model with multipliers constrained to the cone spanned by confidence intervals for the components of the stochastic benchmark vector at varying probability levels. Consider a polyhedral assurance region based upon bounded pairwise ratios between multipliers. It is shown that in general it is never possible to identify a center-vector defined as a vector in the interior of the cone with identical angles to all extreme rays spanning the cone. Smooth cones are suggested if an asymmetric variation in the set of feasible relative prices is to be avoided.     

Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data

This paper discusses a factor model for short-term forecasting of GDP growth using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm, combined with a principal components estimator. We discuss some in-sample properties of the estimator in a real-time environment and propose alternative methods for forecasting quarterly GDP with monthly factors. In the empirical application, we use a novel real-time dataset for the German economy. Employing a recursive forecast experiment, we evaluate the forecast accuracy of the factor model with respect to German GDP. Furthermore, we investigate the role of revisions in forecast accuracy and assess the contribution of timely monthly observations to the forecast performance. Finally, we compare the performance of the mixed-frequency model with that of a factor model, based on time-aggregated quarterly data.     

Buyer power and supplier incentives

This paper analyzes the origins and welfare consequences of buyer power. We show that if suppliers are capacity constrained or have strictly convex costs, there are two different channels through which large buyers can obtain more favorable terms from their suppliers. In particular, we show how the presence of large buyers can then erode the value of suppliers’ outside option. Somewhat surprisingly, we show how this can induce suppliers to undertake strategies that lead to higher output and potentially higher welfare.     

Wages, Experience and Seniority

In this paper we study the sources of wage growth. We identify the contribution to such growth of general, sector specific and firm specific human capital. Our results are interpretable within the context of a model where the returns to human capital may be heterogeneous and where firms may offer different combinations of entry level wages and firm specific human capital development. We allow for the possibility that wages are match specific and that workers move jobs as a result of identifying a better match. To estimate the average returns to experience, sector tenure and firm specific tenure within this context, we develop an identification strategy which relies on the use of firm closures. Our data source is a new and unique administrative data-set for Germany that includes complete work histories as well as individual characteristics. We find positive returns to experience and firm tenure for skilled workers. The returns to experience for unskilled workers are small and insignificant after 2 years of experience. Their returns to sector tenure are also zero. However, their returns to firm tenure are substantial.     

Transparency on the consumer side and tacit collusion

This paper investigates the effects on tacit collusion of increased market transparency on the consumer side of a market in a differentiated Hotelling duopoly. Increasing market transparency increases the benefits to a firm from undercutting the collusive price. It also decreases the punishment profit. The net effect is that collusion becomes harder to sustain. In the limiting homogeneous market, the effect vanishes. Here market transparency does not affect the possibilities for tacit collusion.     

Competition and managerial autonomy

I formalize and challenge the idea that competition forces managers to make better choices, thus favoring managerial autonomy in decision making. If managers care about keeping their position or avoiding interference, and they can make strategic choices that affect both expected firm profits and their riskiness, even if competition at first pushes the manager towards profit maximization, further increases in competition might lead him to take excessive risks. To curb this possibility, the principal owner optimally reduces the degree of autonomy granted to the manager. Hence, higher levels of managerial autonomy are more likely for intermediate levels of competition.     

What Drives Fraud in a Credence Goods Market? – Evidence from a Field Study

This paper investigates the impact of competition on an expert firm's incentive to defraud its customers in a credence goods market. Controlling for the competence of car repair shops, their financial situation, and reputational concerns, we use and complement the data set from a nationwide field study conducted by the German Automobile Association that regularly checks the reliability of garages in Germany. We find that more intense competition lowers a firm's incentive to defraud its customers.     

An efficient nonparametric estimator for models with nonlinear dependence

We provide a convenient econometric framework for the analysis of nonlinear dependence in financial applications. We introduce models with constrained nonparametric dependence, which specify the conditional distribution or the copula in terms of a one-dimensional functional parameter. Our approach is intermediate between standard parametric specifications (which are in general too restrictive) and the fully unrestricted approach (which suffers from the curse of dimensionality). We introduce a nonparametric estimator defined by minimizing a chi-square distance between the constrained densities in the family and an unconstrained kernel estimator of the density. We derive the nonparametric efficiency bound for linear forms and show that the minimum chi-square estimator is nonparametrically efficient for linear forms.     

CHOOSING THE VARIABLES TO ESTIMATE SINGULAR DSGE MODELS

We propose two methods to choose the variables to be used in the estimation of the structural parameters of a singular DSGE model. The first selects the vector of observables that optimizes parameter identification; the second selects the vector that minimizes the informational discrepancy between the singular and non‐singular model. An application to a standard model is discussed and the estimation properties of different setups compared. Practical suggestions for applied researchers are provided. Copyright © 2014 John Wiley & Sons, Ltd.     

Inconsistency of the MLE and inference based on weighted LS for LARCH models

This paper considers a class of finite-order autoregressive linear ARCH models. The model captures the leverage effect, allows the volatility to be arbitrarily close to zero and to reach its minimum for non-zero innovations, and is appropriate for long memory modeling when infinite orders are allowed. However, the (quasi-)maximum likelihood estimator is, in general, inconsistent. A self-weighted least-squares estimator is proposed and is shown to be asymptotically normal. A score test for conditional homoscedasticity and diagnostic portmanteau tests are developed. Their performance is illustrated via simulation experiments. It is also investigated whether stock market returns exhibit some of the characteristic features of the linear ARCH model.