Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data

In this paper, we develop a methodology for forecasting key macroeconomic indicators, based on business survey data. We estimate a large set of models, using an autoregressive specification, with regressors selected from business and household survey data. Our methodology is based on the Bayesian averaging of classical estimates method. Additionally, we examine the impact of deterministic and stochastic seasonality of the business survey time series on the outcome of the forecasting process. We propose an intuitive procedure for incorporating both types of seasonality into the forecasting process. After estimating the specified models, we check the accuracy of the forecasts.     

On the hellwig method of feasible portfolio construction

The work feasible portfolio is built into the work, that is, the k-dimensional Q column vector with components q_i where q_i 0 for i=1,...,k and q₁+...+q_k=1. We define i=1,...,k in the following way:

Primary school students' interests in STEM careers: how conceptions of STEM professionals and gender moderation influence

International Journal of Technology and Design Education - This study assessed elementary school students’ conceptions of STEM professions and its potential influence on STEM career interest,...     

Effects of the matching fund program on IPO and bankruptcy of SMEs in Korea

Most small and medium enterprises (SMEs) in Korea struggle in financing R&D. Thus, the Korea Core Industrial Technology Investment Association (KITIA) was established to implement a matching fund program (MFP) to help SMEs with this undertaking. Applicant firms to the MFP are reviewed first based on their technology levels and are required to attract financing from venture capitalists (VCs), before receiving funds from the MFP. This paper, based on KITIAs MFP applicant data from 2001 to 2004, evaluates the performance of the MFP in terms of the success rate of funded SMEs’ initial public offerings (IPOs) and bankruptcy rates in relation to those SMEs that could not attract matching funds. Propensity score matching was used to eliminate selection bias from the data in order to assess the actual treatment effect of the MFP. According to the result of logistic regression applied to the matched data, MFP apparently did not have significant effects on IPO success and bankruptcy. This implies that MFP needs to allow VCs more time to evaluate SMEs covering qualitative aspects as well. It is expected that this change of structure can provide better performance of MFP in terms of IPO success and bankruptcy of matched SMEs.     

Money is Power: Monetary Intelligence—Love of Money and Temptation of Materialism Among Czech University Students

In this study, we develop a theoretical model of monetary intelligence (MI), explore the extent to which individuals’ meaning of money is related to the pursuit of materialistic purposes, and test our model using the whole sample and across college major and gender. We select the 15-item love of money (LOM) construct—Factors Good, Evil (Affective), Budget (Behavioral), Achievement, and Power (Cognitive)—from the Money Ethic Scale and Factors Success and Centrality and two indicators—from the Materialism Scale. Based on our data collected from 330 university students in Czech Republic, we provide the following findings. First, our formative models are superior to our reflective models. Second, for the reflective model, money represents Power, Good, Achievement, and not Evil, in the context of materialism. Our formative model suggests that those who pursuit materialism cherish Achievement (vanity) but Budget their money poorly. Third, multi-group analyses illustrate that humanities students (62.4 % female) consider money as Evil and Budget their money poorly, while those in natural sciences (37.6 % female) do not. Further, men are obsessed with Achievement, whereas women do not Budget their money properly, suggesting reflective temptation for males and impulsive temptation for females. Our novel discoveries shed new lights on the relationships between LOM and materialism and offer practical implications to the field of consumer behavior and business ethics.     

Applying the Randomized Response Technique in Business Ethics Research: The Misuse of Information Systems Resources in the Workplace

Mitigating response distortion in answers to sensitive questions is an important issue for business ethics researchers. Sensitive questions may be asked in surveys related to business ethics, and respondents may intend to avoid exposing sensitive aspects of their character by answering such questions dishonestly, resulting in response distortion. Previous studies have provided evidence that a surveying procedure called the randomized response technique (RRT) is useful for mitigating such distortion. However, previous studies have mainly applied the RRT to individual dichotomous questions (e.g., yes/no questions) in face-to-face survey settings. In this study, we focus on behavioral research examining the relationships between latent variables, which are unobserved variables measured by multiple items on Likert or bipolar scales. To demonstrate how the RRT can be applied to obtain valid answers from respondents answering a self-administered online questionnaire with Likert and bipolar scales, we build a behavioral model to study the effect of punishment severity on employees’ attitudes toward misuse of information systems resources in the workplace, which in turn influence misuse behavior. The survey findings meet our expectations. The respondents are generally more willing to disclose sensitive data about their attitudes and actual behavior related to misuse when the RRT is implemented. The RRT’s implications for causal modeling and the advantages and challenges of its use in online environments are also discussed.