Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track using a specific form of intercept correction. Among these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and subsequent recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment, and very persistent negative effects on trend growth. Similar findings also emerge for forecasts by institutions, for survey forecasts, and for the other G7 countries.     

Individual differences in assessing problem similarity

This paper reports on a study which investigates the potential influence of perceptual differentiation (PD) ability on introductory accounting students' ability to identify and use analogies to solve problems. Accounting students with differing levels of PD ability first studied two sample problems with solutions which provided potential source analogues for analogical transfer. They then attempted to solve two analogous accounting problems differing only in the criteria for identifying problem analogousness. Findings suggest that PD ability may influence a problem solver's ability to identify analogous problems by affecting which problem features (surface or structural) he or she is likely to attend to and use for assessing problem similarity and potential analogousness. The paper concludes by discussing implications for pedagogical practices in accounting.     

What you are is what you like—similarity biases in venture capitalists' evaluations of start-up teams

This paper extends recent research studying biases in venture capitalist's decision making. We contribute to this literature by analyzing biases arising from similarities between a venture capitalist and members of a venture team. We summarize the psychological foundations of such similarity effects and derive a set of hypotheses regarding the impact of similarity on the assessment of team quality. Using data from a conjoint experiment with 51 respondents, we find that venture capitalists tend to favor teams that are similar to themselves in type of training and professional experience. Our results have important implications for academics and practitioners alike.     

Differences in consumer perceptions of similarity and dissimilarity

Researchers have usually treated similarity ratings as the simple inverse of dissimilarity ratings. Tversky's theory of perception of similarity and dissimilarity (1977), however, explicitly recognizes that the attributes used in the perception of similarity and the attributes used in the perception of dissimilarity are decidedly different. In fact, the weights assigned to common and distinctive features changes according to the task at hand (i.e., when similarity is to be rated the common features are weighted more, and when dissimilarity is to be rated the distinctive features are weighted more). This study demonstrates these differences in a judgment task of rating similarity and dissimilarity on the basis of data that is external to the subjects.     

Practice makes perfect: Entrepreneurial-experience curves and venture performance

This study tackles the puzzle of why increasing entrepreneurial experience does not always lead to improved financial performance of new ventures. We propose an alternate framework demonstrating how experience translates into expertise by arguing that the positive experience–performance relationship only appears to expert entrepreneurs, while novice entrepreneurs may actually perform increasingly worse because of their inability to generalize their experiential knowledge accurately into new ventures. These negative performance implications can be alleviated if the level of contextual similarity between prior and current ventures is high. Using matched employee–employer data of an entire population of Swedish founder-managers between 1990 and 2007, we find a non-linear relationship between entrepreneurial experience and financial performance consistent with our framework. Moreover, the level of industry, geographic, and temporal similarities between prior and current ventures positively moderates this relationship. Our work provides both theoretical and practical implications for entrepreneurial experience—people can learn entrepreneurship and pursue it with greater success as long as they have multiple opportunities to gain experience, overcome barriers to learning, and build an entrepreneurial-experience curve.     

Forecasting another’s enjoyment versus giving the right answer: Trust, shared values, task effects, and confidence in improving the acceptance of advice

In two experiments, participants received advice from another participant on a task either with a correct answer (intellective tasks) or without a correct answer (judgmental task), in which the participant had to make a forecast. In both experiments, the level of trust in the advisor and a perception of the advisor having similar values were important predictors of the acceptance of advice for a judgmental, taste forecast task, whereas advisor confidence was a more important predictor of the acceptance of advice on the intellective task. In Experiment 2, the face-to-face interactions between the decision-maker and the advisor were videotaped and coded. Advisors provided more information to decision-makers for the taste forecast than for the intellective task. Further, whether the advisor provided information to supplement their recommendation or not was a significant predictor of the acceptance of advice on the taste forecast, but not on the intellective task. The results are discussed in the context of previous research on advice, which has predominately used intellective tasks.     

Observation of other's preferential treatment as a constructive catalyst for improved repatronize intentions

Observing others receive preferential treatment has been found to provoke bystanders' negative reactions. However, seeing others treated better may also inspire bystanders to increase their repatronize intentions. Drawing upon theories of social comparison and goal-setting, this study investigates the condition when watching another's preferential treatment acts as a catalyst to enhance bystanders' repatronize intentions and the mechanism by which the constructive effect appears. The results indicate that when bystanders witness similar others' preferential treatment and believe that they can attain comparable treatment, their goal-setting motivation is more activated and consequently they are more committed to goal-relevant behavior (i.e., repatronize intentions).     

基于内容查询的图像数据多层模型和快速检索研究

基于内容的图像搜索是目前搜索领域的研究热点,取得了较大的成果,在实际的开发中对于图像的数据库存储和检索的要求较高。本文通过对图像特征的研究,采用了面向对象的多层数据模型,并分析了索引空间中的多维索引降维、向量空间、度量空间等方法,引出了基于预分类和预计算相似度、树索引和二分快速检索图像数据的方法。接着通过分析其工作流程,从数据模型和检索方法上进行了论述,构造了基于内容的图像检索的工作架构。本文总结了此方案的创新点和适用范围,从而在理论证明了它的优秀的检索性能。最终提出了采用此方案进行基于内容的图像查询的可行途径。     

产业技术链视角下研发合作伙伴识别研究

在研发合作伙伴识别中，如何依据产业技术链不同环节的企业技术优势识别研发合作伙伴是一个新问题。为此，提出基于产业技术链的研发合作伙伴识别研究框架，首先，划分产业技术链，描绘技术与企业的产业技术链布局。然后，提出互补性原则下的产业链潜在研发合作伙伴识别，以及相似性原则下的技术链潜在研发合作伙伴识别。最后，以动力电池产业中的丰田公司为例进行实证研究，验证了识别方法的有效性。     

一种辐射源多特征数据关联的新方法

提出了一种辐射源多特征数据关联的新方法，对数据关联的相似性度量进行了讨论，给出了一种新的多特征参量统计相似性度量，并用谱系聚类法作数据关联，对所得关联结果进行了统计检验，计算机仿真结果证实了本方法的有效性。