Inhomogeneous Radiation Boundary Conditions Simulating Incoming Acoustic Waves for Computational Aeroacoustics

A set of nonhomogeneous radiation and outflow conditions which automatically generate prescribed incoming acoustic or vorticity waves and, at the same time, are transparent to outgoing sound waves produced internally in a finite computation domain is proposed. This type of boundary condition is needed for the numerical solution of many exterior aeroacoustics problems. In computational aeroacoustics, the computation scheme must be as nondispersive ans nondissipative as possible. It must also support waves with wave speeds which are nearly the same as those of the original linearized Euler equations. To meet these requirements, a high-order/large-stencil scheme is necessary The proposed nonhomogeneous radiation and outflow boundary conditions are designed primarily for use in conjunction with such high-order/large-stencil finite difference schemes

Excess Return of US Mutual Funds

The paper examines the factors that contribute to the outperformance of mutual funds in relation to the market, with a particular emphasis on the macroeconomic indicators as the key variables of interest. The paper begins by providing a comprehensive literature review on various factors that can impact the performance of mutual funds. The discussion encompasses a wide range of topics, including skill presence, diseconomies of scale, and other challenges associated with generating excess returns for investors.In the second part of the paper, an empirical analysis is conducted using actively managed US mutual funds to establish a relationship between fund performance and macro-variables, specifically focusing on term and credit spreads. Furthermore, the study considers different returns on positive and negative changes in spreads. The sample consists of funds that primarily invest in various sectors within the United States, with the Standard and Poor's 500 (S&P 500) serving as the benchmark. To assess the performance of funds with active strategies, panel data models are applied, with the excess return over the benchmark as the dependent variable. Different subperiods, including the financial crisis and the COVID-19 period, are examined. Notably, the impact of variables during the pandemic period differs significantly from other subperiods. The findings indicate that positive and negative changes in the spread between corporate bond yields have significant and positive effects across almost all periods, which has practical implications for potential investors. It suggests that active professional portfolio managers have been successful in uncertain periods. To control for external shocks and funds' cross-correlation, double-clustered standard errors are employed, and a series of robustness checks confirm the stability of the results

Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg

This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors

Assessment of Higher-Order RANS Closures in a Decelerated Planar Wall-Bounded Turbulent Flow

A reference DNS database is presented, which includes third- and fourth-order moment budgets for unstrained and strained planar channel flow. Existing RANS closure models for third- and fourth-order terms are surveyed, and new model ideas are introduced. The various models are then compared with the DNS data term by term using a priori testing of the higher-order budgets of turbulence transport, velocity-pressure-gradient, and dissipation for both the unstrained and strained databases. Generally, the models for the velocity-pressure-gradient terms are most in need of improvement

Customer Value-Oriented Business Education in The Post-Covid Era: The Case of MBA Programs in Russia

Purpose of the research: The Covid pandemic has been a time of enormous challenges in the management of education, business education included. Transformation in education technologies has been accompanied by changes in consumer values, which education management and marketing should now focus on. This paper investigates factors and parameters involved in formation of perceived customer value with regard to MBA programmes, using experience of e-learning during the Covid-19 pandemic. Methods: To attain the stated goal, survey-based qualitative and quantitative two-stage research was carried out. This involved MBA students at leading Moscow universities who were studying online during the pandemic in 2020–2021 and for whom part-time learning suddenly turned into online learning for the whole period of study. Key results: The research tested the significance of a theoretical approach to educational values (seen as an array of functional, epistemic, social and emotional values), to be integrated into MBA programmes by educational management. The findings revealed the parameters which currently determine the content of each of these four groups of values in MBA programmes, indicating that the structure of programme choice has already been formed. During the pandemic, the most significant parameters of online education market development have been the reputation of the university, the reputation and e-content of the MBA programme, flexible organization of the study process (based on e-technologies) and the availability of an online educational platform. Quantitative analysis enabled the authors to form a mathematical model of integral consumer assessment of usefulness, taking into account the combination of education value factors and their significance for various sociodemographic groups. The findings proved our hypothesis about the significant dependence between sociodemographic characteristics of MBA students and what they value the most, which needs to be taken into account in knowledge management. This outcome can provide a compass for e-learning knowledge as it points to the most relevant direction: clusterization while positioning business education programmes, and implementation of flexible individual e-learning paths when planning educational content

Forecasting oil prices with penalized regressions, variance risk premia and Google data

This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil market. Our evidence shows that penalized regressions provided the best forecasting performances across most of the forecasting horizons. Moreover, we found that models using the VRP as an additional predictor performed best for forecasts up to 6-12 months ahead forecasts, while models using Google data as an additional predictor performed better for longer-term forecasts up to 12-24 months ahead. However, we found that the differences in forecasting performances were not statistically different for most models, and only the Principal Component Regression (PCR) and the Partial least squares (PLS) regression were consistently excluded from the set of best forecasting models. These results also held after a set of robustness checks that considered model specifications using a wider set of influential variables, a Hierarchical Vector Auto-Regression model estimated with the LASSO, and a set of forecasting models using a simplified specification for Google Trends data