Bayesian forecasting of electoral outcomes with new parties’ competition

We propose a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework. Although novel, the methodology builds upon basic statistical structures, largely modern analysis of variance type models, and it is carried out in open-source software. The methodology is motivated by the specific challenges of forecasting elections with the participation of new political parties, which is becoming increasingly common in the post-2008 European panorama. Our methodology is also particularly useful for the allocation of parliamentary seats, since the vast majority of available opinion polls predict at national level whereas seats are allocated at local level. We illustrate the advantages of our approach relative to recent competing approaches using the 2015 Spanish Congressional Election. In general, the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls models. Our forecasts are, in relative terms, particularly accurate in predicting the seats obtained by each political party.     

我国土地财政的区域差异与成因——基于省际面板数据的实证研究

本文旨在揭示我国土地财政的量化特征和区域差异的成因。目前我国省际间土地财政强度差别较大,且在2003-2007年间呈现先下降继而小幅上升的变化轨迹。从东中西三大地带看,土地财政强度呈现＂东高西低＂格局。基于省际面板数据的实证检验结果支持土地财政动因的＂GDP锦标赛说＂而拒绝＂财政压力说＂。正是发展条件不同的各地区围绕GDP增长展开锦标赛,将土地作为招商引资筹码,以及推动城市扩张和大力发展房地产业的载体,从而导致了土地财政的区域差异。     

How should Engel's law be formulated?

Engel's law expresses a “negative stochastic association” of income and the proportion of income that is spent on food. However, there are many quite different notions of “negative stochastic association” and consequently there are different ways of defining Engel's law. We relate these different concepts to Engel's original statistical analysis and show that one must give credit to Engel for the first non-parametric statistical analysis of budget-data.