Economic factors and suicide rates: associations over time in four countries

The original publication is available at www.springerlink.co

Statistically derived contributions of diverse human influences to twentieth-century temperature changes

The warming of the climate system is unequivocal as evidenced by an increase in global temperatures by 0.8 °C over the past century. However, the attribution of the observed warming to human activities remains less clear, particularly because of the apparent slow-down in warming since the late 1990s. Here we analyse radiative forcing and temperature time series with state-of-the-art statistical methods to address this question without climate model simulations. We show that long-term trends in total radiative forcing and temperatures have largely been determined by atmospheric greenhouse gas concentrations, and modulated by other radiative factors. We identify a pronounced increase in the growth rates of both temperatures and radiative forcing around 1960, which marks the onset of sustained global warming. Our analyses also reveal a contribution of human interventions to two periods when global warming slowed down. Our statistical analysis suggests that the reduction in the emissions of ozone-depleting substances under the Montreal Protocol, as well as a reduction in methane emissions, contributed to the lower rate of warming since the 1990s. Furthermore, we identify a contribution from the two world wars and the Great Depression to the documented cooling in the mid-twentieth century, through lower carbon dioxide emissions. We conclude that reductions in greenhouse gas emissions are effective in slowing the rate of warming in the short term.F.E. acknowledges financial support from the Consejo Nacional de Ciencia y Tecnologia (http://www.conacyt.gob.mx) under grant CONACYT-310026, as well as from PASPA DGAPA of the Universidad Nacional Autonoma de Mexico. (CONACYT-310026 - Consejo Nacional de Ciencia y Tecnologia; PASPA DGAPA of the Universidad Nacional Autonoma de Mexico

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

The nonlinear behavior of competition: the impact of talent compression on competition

Competitive balance, Nonlinear, Co-trending, C22, L83, O15,

Comparing Parametric and Semiparametric Binary Response Models

Using data from the German Socio Economic Panel we estimate the effects of several covariates on the probability of becoming unemployed following a completed apprenticeship in Germany by fitting both parametric and semiparametric binary response models. Moreover, we test the adequacy of the hypothesized link function of the parametric model against the semiparametric model by employing a test recently developed by Hardle and Horowitz. have illustrated how XploRe can be used to estimate parametric and semiparametric binary response models using data. In the application at hand, it turned out that the specification of the parametric model could not be rejected. Even though most of the techniques used in this paper have been deveoped just recently they are readily available in the interactive statistical computing environment XploRe. The respective XploRe codes are included in the paper. The research in this paper was supported by Sonderforschungsbereich 373 at Humboldt-University Berli..

Is the Fisher effect non-linear? some evidence for Spain, 1963-2002

In this paper the role of non-linearities in the relationship between nominal interest rates and inflation is examined, in order to shed some additional light on the mostly unfavourable evidence on the presence of a full Fisher effect. The analysis is applied to the case of Spain for the period 1963-2002, which allows previous results on the subject to be re-examined and extended. The empirical methodology makes use of recent developments on threshold cointegration, so that cointegration between a pair of variables should be expected only once a certain threshold was reached.

RESET for quantile regression

Quantile regression, RESET, 62G10,