Are voters rational?

We test whether a voter's decision to cast a vote depends on its probability of affecting the election outcome. Using exogenous variation arising at population cutoffs determining council sizes in Finnish municipal elections, we show that larger council size increases both pivotal probabilities and turnout. These effects are statistically significant, fairly large and robust. Finally, we use a novel instrumental variables design to show that the jumps in the pivotal probabilities are the likely candidate for explaining the increase in turnout, rather than the other observed simultaneous jumps at the council size cutoffs. Moreover, our results indicate that turnout responds only to within-party pivotal probabilities, perhaps because they are more salient to the voters than the between-party ones.     

Unconventional monetary policies and the corporate bond market

The paper uses a reduced-form vector autoregressive framework to study the effects of quantitative easing and operation “twist”, as well as a conventional monetary expansion, on corporate bond yields and spreads. We construct rating- and maturity-based weekly bond portfolios using TRACE and simulate monetary policies as shocks to the Treasury yield curve. We find that none of the policies can persistently lower corporate spreads, and that operation twist is the only policy capable of lowering corporate yields. This latter finding can be accounted for by the operation twist’s ability to keep the monetary base constant and, therefore, to flatten the riskless yield curve without generating inflationary expectations.     

Determinants of revenue per available room: Influential roles of average daily rate,demand, seasonality and yearly trend

Determining price per room to be charged to customers is an important decision to be taken by hotel management. Hotels frequently change their room rates based on the demand of room, occupancy rate, seasonal pattern, and strategies undertaken by other hotels on pricing. We formulated four models to analyse how various influencing variables, such as hotel price, demand, yearly trend and monthly seasonality influence hotel revenue per available room (RevPar). To analyse a case, we used monthly accommodation statistics for Sweden taken for Swedish Agency for Economic and Regional Growth and Statistics from January 2008 to July 2017. We carried out data analysis using both multiple regression and Multivariate Adaptive Regression Splines (MARS) model and found that application of MARS can help establishing a nonlinear relationship of RevPar with other determining variables in a superior way. We also proposed the possibility of developing a better forecasting model using MARS.     

Did foreign exchange holding influence growth performance during the global financial crisis?

In a context of increased foreign exchange reserves holding from emerging and developing countries, this paper investigates the diminishing return of reserves holding assumption over the most severe phase of the global financial crisis (2008Q1–2010Q4). Relying on a Panel Smooth Transition Regression model, we highlight the differential effect of the accumulation of foreign exchange reserves for a set of financial vulnerabilities variables. In a specific manner, although reserves accumulation is effective above a critical threshold to cope with vulnerabilities related to the financial channel, we show that it becomes less effective beyond a certain threshold for domestic bank vulnerabilities. Our results are robust to alternative specifications.     

HOW TO MEASURE THE ECONOMIC IMPACT OF VECTOR‐BORNE DISEASES AT COUNTRY LEVEL

Vector‐borne diseases (VBDs) are widespread in less developed countries and reemerging in developed ones. Available economic studies agree that VBDs have significant effects on countries' economic outcomes, and affirm that a systematic evaluation of such effects is crucial for the efficient allocation of resources to health‐related priorities. This paper provides a comparative assessment of available methodologies for measuring the economic impact of VBDs at national level. We review both macroeconometric and micro‐based approaches, and examine advantages and disadvantages of current methods. We conclude by suggesting possible areas for future research.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.     

How risky is a random process?

The riskiness of random processes is compared by (a) employing a decision theoretic equivalence between processes and lotteries on path-spaces to identify the riskiness of the former with that of the latter, and (b) using the theory of comparative riskiness of lotteries over vector spaces to compare the riskiness of lotteries on a given path-space. We derive the equivalence used in step (a) and contribute a new criterion to the theory applied in step (b). The validity of the new criterion, which applies second order stochastic dominance to utility distributions, is established by showing its equivalence to the benchmark decision theoretic criterion when comparing the riskiness of lotteries over any vector space. We demonstrate the theory’s tractability via diverse economic applications.     

我国最低工资政策的就业效应——基于北京市和重庆市建筑业的分析

VAR模型对最低工资就业效应的分析结果显示:北京市的最低工资标准与建筑业就业只是在数理统计上存在关系,并没有实际的经济意义;最低工资对重庆市建筑业的微弱负影响可以忽略不计。最低工资的就业效应并不明显,可以紧密联系行业平均工资来调整最低工资标准,以保障劳动者的基本生活。     

基于特征增量的新类识别方法研究

在分类应用的过程中,经常会出现新的类别,导致数据分布发生显著变化,使得原分类模型不再适用。如何识别新的类别使分类模型能适应其出现已经成为一个亟需解决的问题。本文提出基于特征增量的SVDD(支持向量数据描述)新类识别方法。该方法在SVDD算法的基础上,通过增加新特征,扩大特征空间维度从而提高模型对于新类的识别能力。在多个数据集上的实验结果表明,该方法能有效识别新类,使更新后的模型具有更高的准确度。