Die Erhebung personenbezogener Gesundheitsdaten bei Dritten

The recently introduced § 213 VVG is linked to an interesting history and regulates an issues that had been discussed in the insurance industry for some time. In its application § 213 VVG is more particular than regulations by the German data protection act, at least concerning the collection of health data. For the processing and usage of data the regulations of the German data protection act still apply. Time will show whether § 213 VVG will lead to a reconciliation of interests of the insured with those of the insurer. The aforementioned two-week-period for the data survey might be a contribution to achieve this goal.     

Is there an asymmetric effect of monetary policy over time? A Bayesian analysis using Austrian data.

The present paper assesses whether monetary policy effects are asymmetric over the business cycle by estimating a univariate model for GDP including additionally the first difference of the 3-month Austrian interest rate as a measure for monetary policy. The asymmetry of the effects is captured by allowing for state-dependent parameters where the latent state variable follows a Markov switching process. The model is estimated within a Bayesian framework using Markov Chain Monte Carlo simulation methods. Model selection and specification tests are performed by means of marginal likelihood. The results document significant negative effects of monetary policy during periods of below-average growth, while the effect seems insignificant during periods of normal or above-average growth. These results corroborate those derived in theoretical models assuming price rigidities and implying a convex supply curve. Additionally, the concern of using appropriate state-identifying restrictions is raised to obtain an unbiased posterior inference. Finally, the analysis concludes by assessing the robustness of the results with respect to alternative measures of monetary policy. First Version Received: December 2000/Final Version Received: May 2001     

Type of earnings management and the effect of ownership structure, firm size, and corporate-governance practices: Evidence from Indonesia

The purpose of this study is to investigate whether companies listed on the Jakarta Stock Exchange (JSE) conduct efficient or opportunistic earnings management and to examine the effect of ownership structure, firm size, and corporate-governance practices on it.Using multiple regressions, we find evidence that the type of earnings management selected by JSE listed firms tends toward efficient earnings management. This evidence is inconsistent with the common view that earnings management in Indonesia is opportunistic. Family ownership has a significant influence on the type of earnings management selected. Firms with a high proportion of family ownership and non-business groups are more inclined to choose efficient earnings management than other types of firms. We find inconsistent evidence with regard to the impact of institutional ownership, firm size, and corporate-governance practices on type of earnings management.     

The university as a social economy: Jane Smiley’sMoo

Jane Smiley's novel, Moo, is reviewed for the models of social economy portrayed therein. The models are those of the faculty, university governance, and the students, as well as the total social economy and valuation process to which the foregoing contribute. The basic story, portrayed in part through tales of the lives and foibles of individuals, is one of resource allocation, organization and control, distribution of gains and sacrifices, and the working out of the purposes, values, and objectives of an institution. Also covered is the tale of a faculty economist who lives his life along the lines of the self-interested rationality model.     

Modeling time-varying higher-order conditional moments: A survey

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time-varying second-order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher-order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time-varying higher-order conditional moments in the last two decades. This paper outlines recent developments in modeling time-varying higher-order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time-varying higher-order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher-order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.