Mathematik, Informatik und Statistik - Open Access LMU - Teil 03/03 Archiv
50 Episoden
1 Minute
Recently academic work has been put forward that argues for a great urgency to implement effective climate policies to stop global warming. Concrete policy proposals for reducing CO2 emissions have been developed by the IPCC. One of the major instruments proposed is a carbon tax. A main obstacle for its implementation, however, are concerns about the short-term effects on employment and output. In order to miti-gate possible negative effects of enviromental taxes on output and employment, several European countries have introduced so-called environmental tax reforms (ETR) which are designed in a budget neutral manner: Revenues from the tax can be used to reduce existing distortionary taxes or to subsidize less polluting activities. We apply this idea to a carbon tax scheme by performing a vector autoregression (VAR) with output and employment data of nine industrialized countries. We impose a simultaneous policy shock on the economy whereby a carbon tax is levied on high-carbon intensive industries and the resulting tax revenue is redistributed to low-carbon intensive industries. Impulse response analysis shows that such a policy allows for net gains in terms of output and employment.
1 Minute
Many face-to-face surveys use field staff to create lists of housing units from which samples are selected. However, housing unit listing is vulnerable to errors of undercoverage: Some housing units are missed and have no chance to be selected. Such errors are not routinely measured and documented in survey reports. This study jointly investigates the rate of undercoverage, the correlates of undercoverage, and the bias in survey data due to undercoverage in listed housing unit frames. Working with the National Survey of Family Growth, we estimate an undercoverage rate for traditional listing efforts of 13.6 percent. We find that multiunit status, rural areas, and map difficulties strongly correlate with undercoverage. We find significant bias in estimates of variables such as birth control use, pregnancies, and income. The results have important implications for users of data from surveys based on traditionally listed housing unit frames.
1 Minute
The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio.
