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This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The objective of this chapter is to analyse the performance of the UiPath (PATH) company on the New York Stock Exchange, in the context of the war between Russia and Ukraine, and to predict the closing price of the PATH stock using autoregressive integrated moving average with (ARIMAX) and without (ARIMA) exogenous variable methods and autoregressive neural networks (NNAR, NNARX).

UiPath has gained a significant reputation in the IT market and has become a point of interest in recent years. However, the current context is marked by an event of international impact, the war between Russia and Ukraine. In this context, this analysis will consider performance from two perspectives: forecasts of the closing price and forecasts of the closing price with an exogenous variable, namely the war between Russia and Ukraine.

In the analysis that follows, we will address a forecast of the stock closing price using ARIMA, ARIMAX, NNAR and NNARX, as well as analysis of changing points and structural breaks of the series.

The changing points in the mean and variance but also the breaks in the structure justify the course of the closing price. From the information extracted in the analysis, it can be concluded that market sentiment is currently pessimistic due to the downward trend in the price. Both the public and the shareholders are disappointed with the performance of PATH stock and are waiting for the next change point that will change the trend of the series.

With the increase in uncertainty around the globe, an intensifying interest is seen in Economic Policy Uncertainty (EPU) as a topic of research. Researchers worldwide understand the significance of the impact of EPU on the country's development. EPU has a far-reaching impact as uncertainty shocks in one part of the world resonate worldwide due to the level of interconnectivity, globalization and quick communication. In order to facilitate these researchers, this study presents a bibliometric analysis of the existing research in this field using VOS viewer software, by consolidating all the studies from Scopus indexed journal articles, conference proceedings and review papers published in English language from 2006 to 2022. Bibliometric analysis on EPU has rarely been performed. The analysis identifies the publication trends, journal-wise citation, most influential authors, countries, institutions, keyword co-occurrence and authors of different countries who have collaborated for the research in the field. Finally, 1,055 papers were used for bibliometric analysis. The findings depicted that the most cited article on EPU is ‘Measuring economic policy uncertainty’ by Baker et al. (2016) and the most prolific author appears to be Rangan Gupta from University of Pretoria which as an institution also has the maximum publications on this topic. The Journal Finance Research Letters has published the greatest number of researches on EPU. This chapter also summarizes the limitations of the study along with new areas of research.

Energy affects all areas of daily life. Especially with the industrial revolution, the fact that manufacturing has become the engine of economic growth has led to a rise in energy consumption. In this process, the countries of the world have increased their economic growth with traditional energy consumption, and this has increased carbon emissions. However, to fulfill the sustainable development goals, both the continuation of economic growth and the reduction of carbon emissions are required. In this context, the substitution of renewable energy consumption in place of traditional energy sources has started to be discussed. The aim of this study is to research the relationships among CO₂ emissions, manufacturing growth, and renewable energy consumption. For this aim, the relationship among carbon emissions, manufacturing growth, and renewable energy consumption is analyzed for the period 1997–2019 in 38 Organisation for Economic Co-operation and Development (OECD) countries. With respect to the findings of autoregressive distributed lag (ARDL) test results, manufacturing growth enhances CO₂ emissions both in the short and long terms. As the proportion of renewable energy consumption in total energy consumption rises, CO₂ emissions decrease both in the short and long terms. On the other hand, according to the Dumitrescu–Hurlin causality test results, there is a one-way causality relationship from carbon emissions to manufacturing growth and from renewable energy consumption to carbon emissions. When the findings are evaluated together, it is understood that renewable energy consumption is a substantial factor in tackling the deadlock of lessening the carbon emissions without adversely impacting manufacturing growth. Therefore, policymakers need to encourage renewable energy consumption.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

Introduction: India has the 15th-largest domestic natural gas consumption (NGC), critical to sustainable economic growth. Promoting natural gas will have a crucial impact on production in all industries.

Purpose: This research gives an overview of NGC and gross domestic product (GDP) in India from 1990 to 2021 and investigates the association and nature of causality between NGC and GDP in India.

Methodology: For the years 1990 through 2021, we used annual statistics from the NGC and the GDP of India. Both research variables data have been taken from the World Bank Indicator.

Findings: There is no causality and correlation between natural gas and GDP in India.

Practical Implications: Based on the research, the Government of India can create different policies for substituting natural gas for other energy sources to have a healthier impact on a sustainable environment in the short and long term. In the future, researchers can work on environmental degradation and GDP.