Uncertainty aversion and aversion to increasing uncertainty

According to the original Ellsberg (1961) examples there is uncertainty version if the decision maker prefers to bet on an urn of known composition rather than on an urn of unknown composition. According to another definition (Schmeidler, 1989), there is uncertainty aversion if any convex combination of two acts is preferred to the least favorable of these acts. We show that these two definitions differ: while the first one truly refers to uncertainty aversion, the second one refers to aversion to increasing uncertainty. Besides, with reference to Choquet Expected Utility theory, uncertainty aversion means that there exists the core of a capacity, while aversion to increasing uncertainty means that the capacity is convex. Consequently, aversion to increasing uncertainty implies uncertainty aversion, but the opposite does not hold. We also show that a completely analogous situation holds for the case of risk and we define a set of risk and uncertainty premiums according to the previous analysis.     

Price Setting With Menu Cost for Multiproduct Firms

We model the decisions of a multiproduct firm that faces a fixed “menu” cost: once it is paid, the firm can adjust the price of all its products. We characterize analytically the steady state firm's decisions in terms of the structural parameters: the variability of the flexible prices, the curvature of the profit function, the size of the menu cost, and the number of products sold. We provide expressions for the steady state frequency of adjustment, the hazard rate of price adjustments, and the size distribution of price changes, all in terms of the structural parameters. We study analytically the impulse response of aggregate prices and output to a monetary shock. The size of the output response and its duration both increase with the number of products; they more than double as the number of products goes from 1 to 10, quickly converging to the response of Taylor's staggered price model.     

Structural Changes in Economic Growth and Well-Being: The Case of Italy’s Parabola

Social Indicators Research - The controversies on the relationship (called ‘gradient’) between the time trend of GDP and of subjective well-being oppose those who claim that the...     

BETWEEN SCYLLA AND CHARYBDIS: INCOME DISTRIBUTION,CONSUMER CREDIT,AND BUSINESS CYCLES

We introduce a macroeconomic model with heterogeneous households and an aggregate banking sector in order to analyze the impact of rising income inequality under different credit scenarios. Growing inequality produces debt‐led consumption boom dynamics when the banking sector is characterized by a lower capital requirement and a higher willingness to lend. Instead, when inequality rises but the banking sector is highly regulated, aggregate demand and output fall. Our results also yield new insights on the appropriate fiscal policy reaction to stabilize the economy: acting on the progressivity of the tax system seems more effective than a proactive countercyclical fiscal policy. (JEL C63, D31, E62, G01)     

The impact of corporate social performance on the cost of debt and access to debt financing for listed European non-financial firms

This study addresses the controversial issue of how non-financial performance affects the cost of debt capital and access to it. The relationship between corporate social performance and two measures of debt cost (accounting-based and market-based) and the measure of debt access are analysed by means of a multi-theoretical framework combining economics with social theories. By observing a sample of listed European non-financial firms over an 8-year period from 2005 to 2012, we find a negative relationship between corporate social performance and interest rate. Consistent with this result, we find a positive relationship between corporate social performance and debt rating. Thus, corporate social performance has a positive role in reducing the cost of debt capital. Moreover, firms with better corporate social performance are more attractive to lenders in terms of leverage allowance. Overall, our findings provide deeper insight into the reasons why companies should improve their corporate social performance.     

A note on empirical likelihoods derived from pairwise score functions

Pairwise likelihood functions are convenient surrogates for the ordinary likelihood, useful when the latter is too difficult or even impractical to compute. One drawback of pairwise likelihood inference is that, for a multidimensional parameter of interest, the pairwise likelihood analogue of the likelihood ratio statistic does not have the standard chi-square asymptotic distribution. Invoking the theory of unbiased estimating functions, this paper proposes and discusses a computationally and theoretically attractive approach based on the derivation of empirical likelihood functions from the pairwise scores. This approach produces alternatives to the pairwise likelihood ratio statistic, which allow reference to the usual asymptotic chi-square distribution and which are useful when the elements of the Godambe information are troublesome to evaluate or in the presence of large data sets with relative small sample sizes. Two Monte Carlo studies are performed in order to assess the finite-sample performance of the proposed empirical pairwise likelihoods.     

Input Variable Selection in Neural Network Models

One of the most important issues in using neural networks for the analysis of real-world problems is the input variable selection problem. This article connects input variable selection with multiple testing in the neural network regression models. In the proposed procedure, the number and the type of input neurons are selected by means of a testing scheme, based on appropriate measures of relevance of a given input variable to the model. In order to avoid the data snooping problem, family-wise error rate is controlled by using the StepM method proposed by Romano and Wolf (2005 Romano , J. P. , Wolf , M. ( 2005 ). Exact and approximate stepdown methods for multiple hypothesis testing . J. Amer. Statist. Assoc. 100 : 94 – 108 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The testing procedure is calibrated by using the subsampling, which is shown to deliver consistent results under weak assumptions on the data generating process and on the structure of the neural network model.