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The monetary policy implementation and corporate investment are closely intertwined. The aim of modern monetary policy is to mitigate economic fluctuations and stabilise economic growth. One of the ways of influencing the real economy is influencing the level of investment by enterprises.

This chapter provides evidence on how monetary policy affected corporate investment in Poland between 1Q 2000 and 3Q 2022. We investigate the impact of Polish monetary policy on investment outlays in contexts of high uncertainty.

Using the correlation analysis and the regression model, we show the relation between the monetary policy and the investment outlays of Polish enterprises. We used the least squares method as the most popular in linear model estimation. The evaluation includes model fit, independent variable significance and random component, i.e. constancy of variance, autocorrelation, alignment with normal distribution, along with Fisher–Snedecor test and Breusch–Pagan test.

We find that Polish enterprises are responsive to changes in monetary policy. Hence, the corporate investment level is correlated with the effects of monetary policy (especially with the decision on the central bank's basic interest rate changes). We found evidence that QE policy has a positive impact on Polish investment outlays. The corporate investment in Poland is positively affected by respective monetary policies through Narodowy Bank Polski (NBP) reference rate, inflation, corporate loans, weighted average interest rate on corporate loans.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

This study aims to investigate the impact of female chairperson, female chief executive officer and presence of females on boards on listed firms’ profitability using data from Ghana.

This study used ordinary least square estimation and generalized least square (i.e. fixed and random effect estimation techniques) estimation on the data of 15 nonfinancial listed firms on Ghana Stock Exchange between 2010 and 2020.

The results show that while males dominate corporate executive positions in listed nonfinancial firms in Ghana, females serving in top corporate executive positions like chief executive officer, board chairperson and female board membership positively impact listed firms’ performance in the form of return on assets, net profit margin and gross profit margin. These findings are consistent even when year and industry effects are controlled for. This suggests that enacting policies at the national and firm levels to encourage female participation in corporate executive roles/positions are critical for promoting firm performance.

This study extends extant empirical literature on the economic role of female executives in firm performance from the developing context of Ghana. With calls in literature for more studies on the subject matter in varied contexts and conditions, this study takes the discussion a step further by investigating whether the gender of those in positions such as board chairperson and chief executive officer matters in firm profitability in Ghana.

The Russian export flow is highly concentrated on few trading partners that results in its high vulnerability to external shock. Furthermore, the Russian–Ukraine conflict and corresponding western sanctions has enhanced the need of export markets diversification for Russia. Therefore, this study is a baseline attempt to explore determinants of export flow along with identifying potential export markets. This objective is realized by employing an augmented version of gravity model on export flow of Russian Federation to 108 trading partners from 2000 to 2020.

The augmented gravity model of export flow is estimated by using employing contemporary panel econometrics such as panel generalized ordinary least square estimation technique with cross-sectional weight along with heteroskedasticity consistent white coefficients is employed to explore impact of selected macroeconomic and policy variables. Furthermore, the sensitivity analysis is performed by using panel random effect along with the Driscoll–Kraay standard errors with pooled ordinary least squares (OLS) regression and random effect generalized least square (GLS) estimator techniques. The estimated result of panel GLS technique is subjected to in-sampled forecasting technique to explore potential export markets.

The findings show that an increase in the income of trading partners and enhancement of domestic production capacity has significant positive impact on Russian export flow, whereas geographic distance has a significant negative impact. Income of trading partners emerged as major determinant of export flow with high explanatory power. Among augmented variables, the real exchange rate reveals a significant positive impact of lower intensity, whereas binary variables for the common border, common history and preferential/free trade agreement show a significant positive impact. The finding of export potential reveals a high concentration of export with existence of large potential for exports across the globe. For instance, many developing countries in Asia, Africa and America reveal high potential for Russian exports.

The findings urge Russian Federation to diversify its export markets by targeting potential export markets. Many emerging developing countries are witnessing a high potential for Russian exports, therefore attempts should be taken to diversify toward them. The expansion of existing transportation facilities along with development of cargo trade can be important policy instrument to realize objective of export diversification.

This study is the first comprehensive analysis that employs augmented gravity model to explore potential export markets for Russian Federation by using panel data of 108 global trading partners from 2000 to 2020. This finding of this study provides a framework of export diversification toward potential markets across the globe.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

This study aims to explore empirically the impact of various factors/determinants on housing prices at the country level as well as in Lahore, the most populous metropolitan city of the most populous province Punjab, Pakistan.

This study uses monthly data ranging from 2013M1 to 2020M1 on variables used in the study. Based on the stationarity results, the method of robust least square is used as an estimation technique. The validity of initial results is also authenticated by canonical cointegration regression.

The empirical result reveals that all included variables significantly affect housing prices both at country level as well as in Lahore. This study found negative impact of regressor age, real exchange rate and urbanization on housing prices, whereas the positive impact of gross domestic product (GDP) per capita, foreign remittances, broad money and real interest rate on housing prices in the case of Pakistan was found. On the other hand, results unveiled the negative impact of regressor age (proportion of population aged between 15 and 64), real exchange rate and urbanization on housing prices, whereas the positive impact of GDP per capita, foreign remittances, broad money and real interest rate on housing prices in Lahore metropolitan city was unveiled.

Based on the extant literature survey, this is a more holistic study of its kind that uncovers the macroeconomic determinants by considering the demand side, supply side and demographic factors of escalated housing prices in Pakistan, so that proper policies can be adopted to keep the housing sector stable. Empirical findings are helpful to acquire an enhanced understanding of how the housing price is determined and form a base for government to tackle the housing affordability problem.

This paper aims to employ a multivariate nonlinear regression analysis to establish a predictive model for the final fracture area, while accounting for the impact of individual parameters.

This analysis is based on the numerical simulation data obtained, using the hybrid finite element–discrete element (FE–DE) method. The forecasting model was compared with the numerical results and the accuracy of the model was evaluated by the root mean square (RMS) and the RMS error, the mean absolute error and the mean absolute percentage error.

The multivariate nonlinear regression model can accurately predict the nonlinear relationships between injection rate, leakoff coefficient, elastic modulus, permeability, Poisson’s ratio, pore pressure and final fracture area. The regression equations obtained from the Newton iteration of the least squares method are strong in terms of the fit to the six sensitive parameters, and the model follow essentially the same trend with the numerical simulation data, with no systematic divergence detected. Least absolutely deviation has a significantly weaker performance than the least squares method. The percentage contribution of sensitive parameters to the final fracture area is available from the simulation results and forecast model. Injection rate, leakoff coefficient, permeability, elastic modulus, pore pressure and Poisson’s ratio contribute 43.4%, −19.4%, 24.8%, −19.2%, −21.3% and 10.1% to the final fracture area, respectively, as they increased gradually. In summary, (1) the fluid injection rate has the greatest influence on the final fracture area. (2)The multivariate nonlinear regression equation was optimally obtained after 59 iterations of the least squares-based Newton method and 27 derivative evaluations, with a decidability coefficient R2 = 0.711 representing the model reliability and the regression equations fit the four parameters of leakoff coefficient, permeability, elastic modulus and pore pressure very satisfactorily. The models follow essentially the identical trend with the numerical simulation data and there is no systematic divergence. The least absolute deviation has a significantly weaker fit than the least squares method. (3)The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.

The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.

The purpose of this paper is to integrate the findings of articles appearing in European Journal of Marketing’s special section on covariance-based versus composite-based structural equations modeling (SEM).

This is an editorial which uses literature review to draw conclusions regarding areas of agreement, areas for further research, and changing the discussion around composite-based SEM methods.

There are now four new areas of agreement regarding composite-based SEM. Researchers should adopt a toolbox approach to their methods and know the strengths and weaknesses of the research tools in their toolbox. Partial least squares (PLS) SEM and covariance-based SEM are not substitutes, and it is inappropriate to use the language of confirmatory factor analysis (CFA) in reporting measurement estimates from PLS SEM. Measurement matters and researchers need to devote effort to using reliable and valid multi-item measures in their investigations.

This postscript article outlines recommendations for authors, reviewers and editors regarding the analysis of data and reporting of results using structural equations models.

Tourism development is critical for economic transformation, particularly in emerging economies. However, the growing spate of terrorism dissuades international tourists, reduces tourism receipts and ultimately hampers the tourism sector's performance. Thus, the government intervenes by altering its military spending to curtail terrorism. Against this backdrop, this study examines the moderating role of military spending in the terrorism–tourism nexus in Nigeria.

The study employs the dynamic ordinary least squares (DOLS) to investigate the moderating role of military spending in the terrorism–tourism nexus in Nigeria. The authors employ the data that cover the period 1995Q1–2019Q4.

The results reveal that terrorism has a catastrophic effect on tourism arrivals in Nigeria while military spending has a positive impact on tourism arrivals. The results further show the moderating role of military spending in the terrorism–tourism nexus is positive and statistically significant. However, the findings are subject to the measures of military spending, terrorism and tourism.

The practical implication of the findings is the need for deliberate and strategic budgeting for the Ministry of Defence to combat terrorism, which should not only focus on the procurement of arms and ammunition but also cover the welfare of the military personnel. Nigeria also needs to formulate and implement necessary tourism policies aimed at countering terrorism in a bid to create and maintain a positive image on the global tourist map.

Many studies, particularly in developing countries like Nigeria, had examined the effect of terrorism on tourism but none has examined the moderating role of military spending in the terrorism–tourism nexus. Hence, this study examines the moderating role of military spending in the relationship between terrorism and tourism in Nigeria, a terrorism-prone country with several tourist sites.

The purpose of this paper is to test if building reputation capital through environmental, social and governance (ESG) investing can mitigate the negative effect of economic policy uncertainty (EPU) on firms’ valuation.

This study uses an unbalanced panel of 591 financial firms between 2005 and 2021 from Canada, France, Germany, Italy, Japan, the United Kingdom (UK) and the USA. Ordinary least square method is used in the empirical tests. To alleviate a potential endogeneity problem, robustness tests are performed using the two-stage least square approach with instrumental variables.

The results of this paper show that sustainable reporting can offset the negative effect of EPU on the valuation of financial firms. Consistent with the stakeholder-based reputation-building hypothesis, sustainability performance may have an insurance-like impact on firms’ valuation during periods of high uncertainty.

According to the findings, during high policy uncertainty periods, investors accept to pay a premium for the stocks of the firms which built social capital through environmental and social investments. Accordingly, it is suggested that regulatory bodies and governments motivate firms to increase their stakeholder orientation to attain higher reputation capital.

Managers can mitigate the negative impact of policy uncertainty on the value of their firms via building social capital, which will increase financial market stability in return, and portfolio investors may use such firms for portfolio optimization decisions.

To the best of the authors’ knowledge, this paper is one of the first to examine the mitigating role of ESG investing on EPU and firm valuation relationships for financial firms. Thus, this study provides new insights related to the impact of ESG performance on valuation during uncertain times.