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Dealing with heterogeneity in panel VARs using sparse finite mixturesHuber, Florian 04 1900 (has links) (PDF)
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
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A Censored Random Coefficients Model for the Detection of Zero Willingness to PayReichl, Johannes, Frühwirth-Schnatter, Sylvia 30 November 2011 (has links) (PDF)
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.
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Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in EuropeCrespo Cuaresma, Jesus, Feldkircher, Martin 07 1900 (has links) (PDF)
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.
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Would DSGE Models have Predicted the Great Recession in Austria?Breuss, Fritz 04 1900 (has links) (PDF)
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.
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Unveiling Covariate Inclusion Structures In Economic Growth Regressions Using Latent Class AnalysisCrespo Cuaresma, Jesus, Grün, Bettina, Hofmarcher, Paul, Humer, Stefan, Moser, Mathias January 2016 (has links) (PDF)
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.
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Body and dieting concerns of pre-adolescent South African girl childrenSmit, Elsa Naomi 01 February 2011 (has links)
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
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Compiler Testing of C11 Atomics for Arm and RISC-VAdolfsson, Hampus January 2022 (has links)
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.
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Spatial Growth Regressions: Model Specification, Estimation and InterpretationLeSage, James P., Fischer, Manfred M. 04 1900 (has links) (PDF)
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)
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The impact of knowledge capital on regional total factor productivityLeSage, James P., Fischer, Manfred M. 04 1900 (has links) (PDF)
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)
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Estimates and inferences of knowledge capital impacts on regional total factor productivityLeSage, James P., Fischer, Manfred M. 07 1900 (has links) (PDF)
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)
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