In this paper, we provide a parsimonious means of estimating panel VARs with stochastic volatility. We assume that coefficients associated with domestic lagged endogenous variables arise from a finite mixture of Gaussian distribution. Shrinkage on the cluster size is introduced through suitable priors on the component weights and cluster-relevant quantities are identified through novel normal-gamma shrinkage priors. To assess whether dynamic interdependencies between units are needed, we moreover impose shrinkage priors on the coefficients related to other countries' endogenous variables. Finally, our model controls for static interdependencies by assuming that the reduced form shocks of the model feature a factor stochastic volatility structure. We assess the merits of the proposed approach by using synthetic data as well as a real data application. In the empirical application, we forecast Eurozone unemployment rates and show that our proposed approach works well in terms of predictions. / Series: Department of Economics Working Paper Series

In this paper we address the problem of negative estimates of willingness to pay. We find that there exist a number of goods and services, especially in the fields of marketing and environmental valuation, for which only zero or positive WTP is meaningful. For the valuation of these goods an econometric model for the analysis of repeated dichotomous choice data is proposed. Our model restricts the domain of the estimates of WTP to strictly positive values, while also allowing for the detection of zero WTP. The model is tested on a simulated and a real data set.

In this paper we put forward a Bayesian Model Averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for uncertainty in spatial linkages. Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS-2 European regions. We show that ignoring uncertainty in the type of spatial weight matrix can have an important effect on the estimates of the parameters attached to the model covariates. After integrating out the uncertainty implied by the choice of regressors and spatial links, human capital investments and transitional dynamics related to income convergence appear as the most robust determinants of growth at the regional level in Europe. Our results imply that a quantitatively important part of the income convergence process in Europe is influenced by spatially correlated growth spillovers.

Dynamic stochastic general equilibrium (DSGE) models are the common workhorse of modern macroeconomic theory. Whereas story-telling and policy analysis were in the forefront of applications since its inception, the forecasting perspective of DSGE models is only recently topical. In this study, we perform a post-mortem analysis of the predictive power of DSGE models in the case of Austria's Great Recession in 2009. For this purpose, eight DSGE models with different characteristics (small and large models; closed and open economy models; one and two-country models) were used. The initial hypothesis was that DSGE models are inferior in ex-ante forecasting a crisis. Surprisingly however, it turned out that not all but those models which implemented features of the causes of the global financial crisis (like financial frictions or interbank credit flows) could not only detect the turning point of the Austrian business cycle early in 2008 but they also succeeded in forecasting the following severe recession in 2009. In comparison, non-DSGE methods like the ex-ante forecast with the Global Economic (Macro) Model of Oxford Economics and WIFO's expert forecasts performed comparable or better than most DSGE models in the crisis.

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

The topic of body image has become widely researched in the past thirty years, but preadolescents have been neglected in this area of research. This dissertation explores the body and dieting concerns of pre-adolescent girls in South Africa in order to address this paucity. A qualitative study was conducted, with data collected via a vignette technique and a semi-structured interview which were analysed thematically. A contradiction was noted between what girls expressed to be true in terms of the importance of appearance and how they perceive those that do not adhere to cultural norms of appearance. Weight and appearance were described as unimportant when evaluating a person, but negative attributes were given to the heavier girl in the vignette, opposed to none to the thinner girl. Appearance-control beliefs also emerged as a salient theme, with participants believing that the heavier girl in the vignette could not help that she was overweight. The latter was interpreted as pity, and masked as empathy, as participants suggested ways in which she could lose weight, and it was expressed that she would be a happier person if she did lose weight. Dieting was a well-known concept among participants, with some stating that they had previously engaged in dieting behaviours. / MA / Dissertation (MA)--University of Pretoria, 2010. / Psychology / unrestricted

The C11 standard introduced atomic types and operations, with an accompanying memory model, to enable the use of shared variables in concurrent programs. In this thesis, I demonstrate how compilers can be tested, in a way that is deterministic and covers the entire set of atomic operations, to ensure they correctly implement C11 atomics and the C11 memory model.  I use a large set of short concurrent programs (”litmus tests”), generated from a model written in a specification language and based on a formalized C11 memory model. Each test program is compiled and run with a model checker, to determine the possible outcomes; any program with an outcome that is possible after compilation but not allowed by C11 is a failed test case. As an alternative to model checking, I also test a nondeterministic, hardware-based method for running tests, but I find that this method is too inaccurate to be useful.  I test IAR and gcc compilers for Arm and RISC-V; all of these compilers pass all tests. Out of three compilers with purposefully inserted bugs, all are correctly identified as faulty. This testing process thus shows some promise, but further evaluation is needed.

This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feed- back nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates. (authors' abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. We provide an econometric derivation of the relationship and show that the presence of latent/unobservable regional knowledge capital leads to a model relationship that includes both spatial and technological dependence. This model specification accounts for both spatial and technological dependence between regions, which allows us to quantify spillover impacts arising from both types of interaction. Sample data on 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)