It often appears some cognitive conflicts among the citizens¡¦ when evaluating the policy outcomes performance about Dengue fever prevention, since existed some characteristics in the input and outcome dimension, it include multi-involvers, multi-goals, multi-values and subjective judgement(Wang Ming-Shen and Chen Cheng-Liaou, 2004: 11). We taked the unfairness measurement approach of the Information Integration Theory (IIT) to explore the policy outcome performance about Dengue fever prevention. In this way, we constructed an algebraic model, namely ¡§Dengue Fever Basic Unfairness Measurement Model,¡¨ with accrute empirical test and validity criteria. According to the way of the factorial graph patterns and statistical interaction test of the model, we may analyze and collect these multiple information of policy outcomes(Farkas, 1991: 61 ; Anderson, 1996: 33). We obtained some important findings, include: 1.The citizen integrated the information of Dengue fever prevention outcomes by ¡¥averging¡¦ model, the experimental data support the mode and it had important policy implications. 2.The effort(implicit factor) of the governer¡¦s devoted to prevention tasks was more than the budget expendures(explicit factor) was the most important factor of outcome performance evaluation, and this had important cognitive implication. 3.According to the differences of the information qulity from the Dengue fever prevention, there was an interpersonal salience about the evaluation of the policy outcome performance. 4.It showed that highly corelation between the habitant¡¦s life modes and the prevention outcomes performance, this implied that the implicit social meaning of Dengue fever prevention were interpersonal interaction. 5.The citizen¡¦s perception of the unfairness measurement for the policy outcome performance of Dengue fever prevention were not satisfaction and sensitive to the interpersonal unfairness situation.

Understanding the determinants of aggregated default probabilities (PDs) has attracted substantial research over the past decades. This study addresses two major difficulties in understanding the determinants of aggregate PDs: Model uncertainty and multicollinearity among the regressors. We present Bayesian Model Averaging (BMA) as a powerful tool that overcomes model uncertainty. Furthermore, we supplement BMA with ridge regression to mitigate multicollinearity. We apply our approach to an Austrian dataset. Our findings suggest that factor prices like short term interest rates and energy prices constitute major drivers of default rates, while firms' profits reduce the expected number of failures. Finally, we show that the results of our baseline model are fairly robust to the choice of the prior model size. / Series: Research Report Series / Department of Statistics and Mathematics

The spread between interest rate and sovereign bond rate is commonly used in- dicator for country's probability to default. Existing literature proposes many different potential spread determinants but fails to agree on which of them are important. As a result, there is a considerable uncertainty about the cor- rect model explaining the spread. We address this uncertainty by employing Bayesian Model Averaging method (BMA). The BMA technique attempts to consider all the possible combinations of variables and averages them using a model fit measure as weights. For this empirical exercise, we consider 20 different explanatory variables for a panel of 47 countries for the 1980-2010 period. Most of the previously suggested determinants were attributed high inclusion probabilities. Only the "foreign exchange reserves growth" and the "exports growth" scored low by their inclusion probabilities. We also find a role of variables previously not included in the literature's spread determinants - "openness" and "unemployment" which rank high by the inclusion probability. These results are robust to a wide range of both parameter and model priors. JEL Classification C6, C8, C11, C51, E43 Keywords Sovereign Spread Determinants, Model Uncer- tainty, Bayesian Model Averaging Author's e-mail semanv()gmail()com...

01 Abstract Foreign exchange reserves are a useful tool and a buffer but maintaining an amount that is too large can be costly to the economy. Recent accumulation of these reserves points to the importance of this topic. This thesis focuses on one specific part of the effect of FX reserves on the economy - the inflation. I use panel data for 74 countries from the year 1996 to the year 2017. There is a certain degree of model uncertainty for which this thesis accounts for by using Bayesian model averaging (BMA) estimation technique. The findings from my model averaging estimations show FX reserves to not be of importance for inflation determination with close to no change when altering lags, variables, when limiting the sample to fixed FX regimes nor when limiting the sample to inflation targeting regimes. The most important variables are estimated to be a central bank financial strength proxy, exchange rate depreciation, money supply, inflation targeting, and capital account openness. These results are robust to lag changes, prior changes, and for the most part remain the same when Pooled OLS is used.