Inflation und Schuldenabbau

The European Central Bank (ECB) recently announced its willingness to do whatever is needed to save the euro. This has raised the question whether such a role of the ECB must lead to higher rates of infl ation. Under current recessive macroeconomic conditions in the eurozone, the ECB’s expansionary monetary policy will not lead to higher infl ation. On the contrary, there is a serious danger of defl ation. Higher infl ation would likely occur only if a permanent stabilisation function were assigned to the ECB. Yet historical examples show that mistakes can be made. During the stagnation in Japan, US economists heavily criticised the Bank of Japan’s timid monetary policy response. But in some sense, current Fed policy seems to be a direct copy of that strategy, caused by uncertainty about the proper communication channel. An infl ation tax could help to bring down the mounting public debt in the wake of the fi nancial crisis, but higher wealth taxes have more transparent distributional effects.     

How social and human capital influence opportunity recognition and resource mobilization in India's handloom industry

Small-scale firms in rural areas play an extremely important role in the development of any country, and especially in developing countries. To understand entrepreneurs who operate in a low-technology industry, we rely on the network perspective on entrepreneurship. In this paper, we investigate how the social and human capital of entrepreneurs (in this case master weavers in the handloom industry) influence their ability to recognize opportunities and mobilize resources. In addition to examining the direct effects, we also explore the possibilities of social capital mediating between human capital, on the one hand, and opportunity recognition and resource mobilization on the other. This paper adds to existing literature in two ways: firstly, we expand the social capital paradigm by including different cultural settings and links to existing studies regarding small enterprises. Secondly, we provide additional evidence to the ongoing debate as to what constitutes a ‘good network’.     

Proportional Odds Models with High‐Dimensional Data Structure

The proportional odds model is the most widely used model when the response has ordered categories. In the case of high‐dimensional predictor structure, the common maximum likelihood approach typically fails when all predictors are included. A boosting technique pomBoost is proposed to fit the model by implicitly selecting the influential predictors. The approach distinguishes between metric and categorical predictors. In the case of categorical predictors, where each predictor relates to a set of parameters, the objective is to select simultaneously all the associated parameters. In addition, the approach distinguishes between nominal and ordinal predictors. In the case of ordinal predictors, the proposed technique uses the ordering of the ordinal predictors by penalizing the difference between the parameters of adjacent categories. The technique has also a provision to consider some mandatory predictors (if any) that must be part of the final sparse model. The performance of the proposed boosting algorithm is evaluated in a simulation study and applications with respect to mean squared error and prediction error. Hit rates and false alarm rates are used to judge the performance of pomBoost for selection of the relevant predictors.     

On the Existence of Chaotic Policy Functions in Dynamic Optimization

Applying the characterization results from Mitra and Sorger (1999), we show that topological chaos is a robust phenomenon in standard aggregative growth models even under arbitrary mild discounting. Furthermore, we state exact discount factor restrictions, under which two of the most popular examples of chaotic dynamics, the logistic map and the tent map, can be optimal policy functions of aggregative growth models.
JEL Classification Numbers: C61, O41.     

Seasonal adjustment of Austrian labour force series

Zusammenfassung Seit mehr als fünfzig Jahren haben Statistiker ihre Anstrengungen darauf gerichtet, optimale Methoden der Saisonbereinigung zu entwickeln. Einige dieser Methoden verwenden wir in der vorliegenden Arbeit, um saisonale Schwankungen aus österreichischen Arbeitsmarktreihen auszuschalten. Saisonbereinigte Reihen spielen für die laufende Konjunkturbeobachtung eine große Rolle. Aber um Enttäuschungen vorzubeugen, möchten wir gleich zu Beginn eine Warnung anbringen. Viele Praktiker scheinen zu glauben, daß bei Verwendung von saisonbereinigten Reihen Veränderungen gegen den Vormonat ein eindeutiges Bild der augenblicklichen Konjunkturlage vermitteln. Dabei wird leider häufig übersehen, daß saisonbereinigte Reihen eine irreguläre Komponente enthalten. In vielen Fällen ist diese Komponente so groß, daß sie die Schwankungen der Reihe von Monat zu Monat dominiert. Es ist daher oft von Vorteil, die jüngsten Daten mit solchen zu vergleichen, die schon zwei oder drei Monate früher anfielen, anstatt sich auf einen Vergleich mit dem Vormonat zu konzentrieren. Der dabei in Kauf genommene Informationsverlust wird durch die größere Verläßlichkeit der Ergebnisse mehr als wettgemacht, und die Information ist noch immer viel früher verfügbar, als wenn man mit Vorjahresvergleichen unbereinigter Daten operiert.

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Financial support by the Jubiläumsfonds der Oesterreichischen Nationalbank under grant no. 2203 is gratefully acknowledged. We wish to thank Manfred Deistler and Wolfgang Polasak for their helpful comments and suggestions.     

The association between two random elements: A complete characterization and odds ratio models

For random elements X and Y (e.g. vectors) a complete characterization of their association is given in terms of an odds ratio function. The main result establishes for any odds ratio function and any pre-specified marginals the unique existence of a corresponding joint distribution (the joint density is obtained as a limit of an iterative procedure of marginal fittings). Restricting only the odds ratio function but not the marginals leads to semi-parmetric association models for which statistical inference is available for samples drawn conditionally on either X or Y. Log-bilinear association models for random vectors X and Y are introduced which generalize standard (regression) models by removing restrictions on the marginals. In particular, the logistic regression model is recognized as a log-bilinear association model. And the joint distribution of X and Y is shown to be multivariate normal if and only if both marginals are normal and the association is log-bilinear.Acknowledgements The author thanks both referees for their helpful comments which improved the first draft of the paper.     

The Impact of Surplus Distribution on the Risk Exposure of With Profit Life Insurance Policies Including Interest Rate Guarantees

This article analyzes the numerical impact of different surplus distribution mechanisms on the risk exposure of a life insurance company selling with profit life insurance policies with a cliquet‐style interest rate guarantee. Three representative companies are considered, each using a different type of surplus distribution: a mechanism, where the guaranteed interest rate also applies to surplus that has been credited in the past, a slightly less restrictive type in which a guaranteed rate of interest of 0 percent applies to past surplus, and a third mechanism that allows for the company to use former surplus in order to compensate for underperformance in “bad” years. Although at the outset all contracts offer the same guaranteed benefit at maturity, a distribution mechanism of the third type yields preferable results with respect to the considered risk measure. In particular, throughout the analysis, our representative company 3 faces ceteris paribus a significantly lower shortfall risk than the other two companies. Offering “strong” guarantees puts companies at a significant competitive disadvantage relative to insurers providing only the third type of surplus distribution mechanism.