Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership

A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler

Tax Preferences, Partisanship and Perceptions of Society: Evidence from Austria

This article systematically investigates the attitudes of voters towards capital taxation and further topics in the realm of the welfare state. We revisit various streams of literature and explore which views, beliefs and perceptions are connected to tax preferences and how these perceptions differ between various voting groups. Special weight is attached to questions of the distribution of income, wealth and opportunities. Inference relies on the outcomes of two large scale online surveys conducted in Austria. Our results suggest that, among others, opinions on fairness in the economic system in general as well as perceptions of inequality are strong predictors of tax preferences in Austria. In addition, these beliefs vary heavily across parties and are thus promising candidates to explain the variation in tax preferences between different voting groups.Series: INEQ Working Paper Serie

Implications of macroeconomic volatility in the Euro area

In this paper we estimate a Bayesian vector autoregressive model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area. This allows us to treat macroeconomic uncertainty as a latent quantity during estimation. Only a limited number of contributions to the literature estimate uncertainty and its macroeconomic consequences jointly, and most are based on single country models. We analyze the special case of a shock restricted to the Euro area, where member states are highly related by construction. We find significant results of a decrease in real activity for all countries over a period of roughly a year following an uncertainty shock. Moreover, equity prices, short-term interest rates and exports tend to decline, while unemployment levels increase. Dynamic responses across countries differ slightly in magnitude and duration, with Ireland, Slovakia and Greece exhibiting different reactions for some macroeconomic fundamentals.Comment: Keywords: Bayesian vector autoregressive models, factor stochastic volatility, uncertainty shocks; JEL: C30, F41, E3

Of clerks & cleaners: the heterogeneous impact of monetary policy on the US labor market

In this paper we estimate the effect of monetary policy on the US labor market using disaggregated data based on large scale micro surveys. By employing a Bayesian factor-augmented vector autoregression framework, we investigate the impact of an unanticipated interest rate change on the unemployment rate in 32 occupation groups. Our results on the aggregate level are in line with the literature and point towards a strong influence of monetary policy on economic activity, overall unemployment and investment. A closer look on the disaggregated level reveals heterogeneous impacts across occupation groups. This heterogeneity can partially be explained by the amount of routine tasks and the degree of offshorability of an particular occupation group. These results suggest that workers who are highly vulnerable to medium-term and long-term developments such as automatization and offshoring are also hit disproportionately hard by short-term economic fluctuations.Series: Department of Economics Working Paper Serie

The heterogeneous impact of monetary policy on the US labor market

We empirically investigate the role of central banks in the context of heterogeneous labor markets, jobless recoveries and job polarization. Specifically, we estimate the effect of monetary policy on the US labor market using disaggregated time series based on large scale survey data. The impact of interest rate changes on unemployment in 32 occupation groups is explored in a Bayesian factor-augmented vector autoregression framework. The results suggest largely heterogeneous impacts across various occupation groups. This heterogeneity can be explained by differential task profiles of the workers in their respective occupations. Workers with tasks that are easily automated or offshored as well as workers at the bottom of the skill distribution are disproportionately affected following a monetary policy shock. This implies that labor market participants that are highly vulnerable to structural developments such as skill-biased technological change and the globalization of labor markets are also most sensitive to conventionalmonetary policy measures. From a policy perspective, we conclude that central banks are unlikely to beable to take on a stabilizing role in the context of labor market polarization

Land and poverty: the role of soil fertility and vegetation quality in poverty reduction

The debate on the land-poverty nexus is inconclusive, with past research unable to identify the causal dynamics. We use a unique global panel dataset that links survey and census derived poverty data with measures of land ecosystems at the subnational level. Rainfall is used to overcome the endogeneity in the land-poverty relationship in an instrumental variable approach. This is the first global study using quasi-experimental methods to uncover the degree to which land improvements matter for poverty reduction. We draw three main conclusions. First, land improvements are important for poverty reduction in rural areas and particularly so for Sub-Saharan Africa. Second, land improvements are pro-poor: poorer areas see larger poverty alleviation effects due to improvements in land. Finally, irrigation plays a major role in breaking the link between bad weather and negative impacts on the poor through reduced vegetation growth and soil fertility

Ultimate P\'olya Gamma Samplers -- Efficient MCMC for possibly imbalanced binary and categorical data

Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on P\'olya Gamma random variables for a range of commonly encountered discrete choice models. From these latent variable representations, new Gibbs sampling algorithms for binary, binomial and multinomial logistic regression models are derived. All models allow for a conditionally Gaussian likelihood representation, rendering extensions to more complex modeling frameworks such as state space models straight-forward. However, sampling efficiency may still be an issue in these data augmentation based estimation frameworks. To counteract this, MCMC boosting strategies are developed and discussed in detail. The merits of our approach are illustrated through extensive simulations and a real data application

Motor Vehicle Density and Air Pollution in Greater Cairo : Fuel Subsidy Removal and Metro Line Extension and Their Effect on Congestion and Pollution

This report answers two questions: What is the statistical relationship between vehicle density in the streets of Greater Cairo and ambient air pollution in the city? And what are the effects of—one, the opening in recent years of another metro line and an extension to it, and two, the recent increases in fuel prices—on vehicle density and ambient air pollution