Economics and Finance

This research examines capital income taxation for a loss averse investor under some acceptable in the literature reference levels relative to which are the changes in the level of wealth valued. Depending on the reference level, some results indicate that it could be possible for a capital income tax increase not to stimulate risk taking even if the tax code provides the attractive full loss offset provisions. However, risk taking can be stimulated if the investor interprets part of the tax as a loss instead as a reduced gain. Then investor becomes risk seeking and moves away from the discomfort zone of relative losses. This later response to taxation causes private risk taking to increase which is contrary to what evolves from assuming an expected utility model. Finally, a number of other reference standards are examined as well.

This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strongly consistent for a large class of data generating processes.

We study the relationship between education and fertility, exploiting compulsory schooling reforms in Europe as source of exogenous variation in education. Using data from 8 European countries, we assess the causal effect of education on the number of biological kids and the incidence of childlessness. We find that more education causes a substantial decrease in childlessness and an increase in the average number of children per woman. Our findings are robust to a number of falsification checks and we can provide complementary empirical evidence on the mechanisms leading to these surprising results.

We study the contribution of health-related behaviors to the health-education gradient by distinguishing between short-run and long-run mediating effects:while in the former only current or lagged behaviors are taken into account, in the latter we consider the entire history of behaviors. We use an empirical approach that addresses the endogeneity of education and behaviors in the health production function. Focusing on self-reported poor health as our health out-come, we find that education has a protective effect for European males and females aged 50+. We also find that the mediating effects of health behaviors - measured by smoking, drinking, exercising and the body mass index – account in the short run for 17% to 31% and in the long run for 23% to 45% of the entire effect of education on health, depending on gender.

We investigate the impact of self-organized reputation versus certification by an independent institution on demand for online shops. Using data from a large Austrian price comparison site, we show that quality seals issued by a credible and independent institution increase demand more than feedback-based reputation. This result is important for markets where the market-maker must deal with issues of asymmetric information concerning the quality of goods and services in the market.

The authors investigate the employment consequences of minimum wage regulation in 16 OECD countries, 1970-2008. Their treatment is motivated by Neumark and Wascher’s (2004) seminal cross-country study. Apart from the longer time interval examined, a major departure is the authors’ focus on prime-age females, a group often neglected in the minimum wage literature. Another is their deployment of time-varying policy and institutional regressors. The average effects they report are consistent with minimum wages causing material employment losses among the target group. Their secondary finding is that minimum wage increases are more associated with (reduced) participation rates than with elevated joblessness. Further, although the authors find common ground with Neumark and Wascher as regards the role of some individual labor market institutions and policies, they do not observe the same patterns in the institutional data. Specifically, prime-age females do not exhibit stronger employment losses in countries with the least regulated markets.

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the smoothness component. The HP smoothing approach requires a linear regression model with a Bayesian conjugate multi-normal-gamma distribution. The Bayesian approach also allows to make predictions of the HP smoother on both ends of the time series. Furthermore, we show how Bayes tests can determine the order of smoothness in the HP smoothing model. The extended HP smoothing approach is demonstrated for the non-stationary (textbook) airline passenger time series. Thus, the Bayesian extension of the HP model defines a new class of model-based smoothers for (non-stationary) time series and spatial models.

In evaluating prediction models, many researchers flank comparative ex-ante prediction experiments by significance tests on accuracy improvement, such as the Diebold-Mariano test. We argue that basing the choice of prediction models on such significance tests is problematic, as this practice may favor the null model, usually a simple benchmark. We explore the validity of this argument by extensive Monte Carlo simulations with linear (ARMA) and nonlinear (SETAR) generating processes. For many parameter constellations, we find that utilization of additional significance tests in selecting the forecasting model fails to improve predictive accuracy.

The extended Hodrick-Prescott (HP) method was developed by Polasek (2011) for a class of data smoother based on second order smoothness. This paper develops a new extended HP smoothing model that can be applied for spatial smoothing problems. In Bayesian smoothing we need a linear regression model with a strong prior based on differencing matrices for the smoothness parameter and a weak prior for the regression part. We define a Bayesian spatial smoothing model with neighbors for each observation and we define a smoothness prior similar to the HP filter in time series. This opens a new approach to model-based smoothers for time series and spatial models based on MCMC. We apply it to the NUTS-2 regions of the European Union for regional GDP and GDP per capita, where the fixed effects are removed by an extended HP smoothing model.

Representation of continuous-time ARMA, CARMA, models is reviewed. Computational aspects of simulating and calculating the likelihood-function of CARMA are summarized. Some numerical properties are illustrated by simulations. Some real data applications are shown.