Symbolic regression for better specification

This note introduces the concept of symbolic regression (SR) to tourism and hospitality research. SR uses genetic programming to find the model that best fits the data without a need to pre-specify a functional form or to impose a certain model as a starting point. In other words, SR helps to uncover the intrinsic characteristics of the data at hand. Our view is that SR can serve as an improved method of testing for misspecification. In this note, we propose to derive the true functional form of the residual using SR. We then use this information to improve the forecasts of the linear regression model and, to perform hypothesis tests if needed.     

Semiparametric time series models driven by latent factor

We introduce a class of semiparametric time series models (SemiParTS) driven by a latent factor process. The proposed SemiParTS class is flexible because, given the latent process, only the conditional mean and variance of the time series are specified. These are the primary features of SemiParTS: (i) no parametric form is assumed for the conditional distribution of the time series given the latent process; (ii) it is suitable for a wide range of data: non-negative, count, bounded, binary, and real-valued time series; (iii) it does not constrain the dispersion parameter to be known. The quasi-likelihood inference is employed in order to estimate the parameters in the mean function. Here, we derive explicit expressions for the marginal moments and for the autocorrelation function of the time series process so that a method of moments can be employed to estimate the dispersion parameter and also the parameters related to the latent process. Simulated results that aim to check the proposed estimation procedure are presented. Forecasting procedures are proposed and evaluated in simulated and real data. Analyses of the number of admissions in a hospital due to asthma and a total insolation time series illustrate the potential for practical situations that involve the proposed models.     

不确定性容忍度、决策逻辑与突破式创新关系研究

以位于武汉市产业园、孵化器、创业园等多家公司的255位产品开发及技术研发人员、市场开发人员为有效样本，采用层级回归方法探讨不确定性容忍度、决策逻辑与突破式创新的关系。结果表明:不确定性容忍度与突破式创新显著正相关；不确定性容忍度与因果逻辑显著负相关，与效果逻辑显著正相关；突破式创新与因果逻辑显著负相关，与效果逻辑显著正相关；因果/效果决策在不确定性容忍度与突破式创新关系中发挥部分中介作用；不确定性容忍度正向调节效果逻辑与突破式创新的关系，对因果逻辑与突破式创新的关系没有显著调节作用。研究结论对企业创新管理实践具有重要指导价值。     

When do B2B brands influence the decision making of organizational buyers? An examination of the relationship between purchase risk and brand sensitivity

The dominant perspective on organizational buying behavior suggests that buyers tend to rely on objective criteria when making product choice decisions and that the potential influence of subjective cues, such as brands, on buyer decision making decreases with increasing risk. An alternative perspective, confirmed in this study by in-depth interviews with various managers, suggests that brands serve as a risk-reduction heuristic, whereby the influence of brands on decision making increases as a function of risk. Building on risk and information processing theories, this research builds on these complementary perspectives to propose that risk and brand sensitivity relate in a U-shaped manner, where brand sensitivity is highest in relatively low- or high-risk situations. The results of scenario- and survey-based field studies—involving 206 and 180 members of buying centers, respectively—suggest that both perspectives have merit and support the proposed nonlinear relationship. Moreover, the findings reveal that the risk-brand sensitivity relationship is moderated by competitive intensity, such that the linear (negative) and quadratic (positive) effects are stronger when competitive intensity is low.     

城乡居民收入差距对居民消费影响的实证分析

回归分析证明了改革开放以来城乡居民收入差距过大导致整个消费水平越来越低,增加城市居民的收入不能提高我国居民的整体消费水平;城镇居民收入较高,相对于农村居民来说边际消费倾向要低于农村居民的边际消费倾向,如果能增加农村居民的收入,则会提高全国的消费总量及转变农村的消费结构。     

我国上市公司财务困境预测模型分析——基于我国A股上市公司研究

基于我国A股市场上市的ST公司和相对应的非ST公司,用Logistic回归的进入法和逐步法对上市公司财务困境预测模型进行了比较分析,得出结论:无论是模型的有效性,还是模型的判断准确率方面,Logistic回归的进入法均优于逐步法。     

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.     

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.     

A regression discontinuity approach to measuring the effectiveness of oil and natural gas regulation to address the common-pool externality

Oil and natural gas reservoirs typically span multiple productive leases so that no owner has rights to the entire stock of resource, resulting in production externalities. Previous literature has examined the effectiveness of government regulation in Texas and Oklahoma in abating these externalities, finding Oklahoma to be more successful in unifying common pools and securing property rights. Using regression discontinuity design, we quantify the impact of regulatory difference between the two states. We find that Oklahoma produces an average of 3361 more barrels of oil over the life of a well, relative to Texas. Given the maturity of the fields in question, the result underscores the continuing importance of addressing common pool externalities even after the primary phase of recovery has largely been completed.