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Papadopoulos A: Two-Tier Stochastic
Frontier Analysis for the Social Scienc

Two-Tier Stochastic Frontier Analysis for the Social Sciences

Lecture Notes in Economics and Mathematical Systems

This volume would prove to be a beneficial textbook option for economics faculties wishing to offer a PhD level course on quantitative developmental economics or applied economics. This reference volume offers a gentle introduction to various state-of-the-art mathematical and econometric modelling techniques with detailed applications. Each chapter, written by experienced scholars, describe a particular quantitative method for economic modelling and policy analysis. The chapters not only cover the theoretical foundations in a student-friendly language but also provide step-by-step guide to computational aspects. Real-world applications, given in each chapter, focus on the socio-economic conditions and well-being of people in the developing economies and middle-income countries.

Quantitative Methods for Applied Development Economics and Policy Analysis

This book provides answers to analysts’ need to know about elasticities of demand, with a unique two-track approach. The first gives a self-contained, non-technical rendition, accessible to those with limited background. The second track contains the underlying theory, derivations, and some more advanced material. Subsequent chapters deal with the demand for food and other groups of goods, the system-wide approach, and the demand side of EDM and CGE models. This convenient source book will be valuable to students, consultants, business economists and government policy analysts. Clarity of exposition and economic intuition is emphasised throughout.
"In this remarkable book, the authors bridge the gap between sophisticated consumer demand theory and the practical world of economic modeling. Their guidance will be invaluable to anyone aiming to shed light on the economic impact of public policies."
Thomas W. Hertel, Distinguished Professor and Executive Director, Center for Global Trade Analysis, Purdue University
 "The elasticity of demand is a key parameter in economics - whether for micro studies or large-scale models. This book illuminates on short-cut approaches for measuring this elasticity and provides derivations and applications. It is a valuable resource for both students and seasoned economists."
Paul Burke, Professor and Head, Arndt-Corden Department of Economics and Deputy Director, Crawford School of Public Policy, ANU College of Law, Governance and Policy, Australian National University
 "This book endears itself to me with its honest approach to determining elasticities for use in policy analysis. In Chapter 1, it tells us about the rule of one half: if you can’t do any better, set own price elasticities for household demands for broadly defined commodities at -0.5. Then through ten chapters, Ken Clements and his co-authors demonstrate that you can do better by combining economic theory with relatively straightforward econometrics. The book deserves wide readership among applied economic modellers who want to take their field forward by supplementing judgement with statistical evidence."
Peter Dixon, AO, Professor, Centre of Policy Studies, Victoria University

Short-cut Demand Elasticities and Other Convenient Approaches to Consumer Demand

In recent decades, the evolution of digital technologies has led to the proliferation of highly distributed, dynamic, and heterogeneous information and communication systems (ICS). Among them, 5G-IoT infrastructures, with their inherently dense topologies, variable load patterns, and heightened vulnerability to cyber threats, have introduced new challenges in ensuring resource efficiency, operational continuity, and dependable performance. These systems increasingly underpin critical domains such as – Industrial automation, smart energy, transportation, and healthcare–where failures or delays are intolerable. Consequently, the demand for robust analytical models capable of capturing the complexity of such infrastructures has never been greater.

This monograph emerged from the recognition that conventional queuing theory frameworks, while effective in classical settings, often fall short when applied to multi-channel systems with synchronisation mechanisms, heterogeneous service policies, and non-Poissonian traffic flows–features that are now typical in modern ICS architectures. To address this gap, the work explores and generalises a class of systems inspired by the "divide and conquer" (DC) paradigm. By formalising DC-like queuing systems, in which incoming requests are decomposed into multiple subrequests, processed in parallel, and synchronised upon completion, the study provides a coherent mathematical foundation for analysing their behaviour under both nominal and stress conditions.

The first part of the monograph establishes the conceptual and mathematical core of DC-like systems, including well-known configurations such as Split-Merge (SM), Fission-Fusion (FF), Team Service Models (TSM), and Independent Server Models (ISM). These systems are studied through rigorous formulations using two-dimensional Markov chains, exact generating functions, and the evaluation of response time and synchronisation delay. In particular, special attention is given to the interdependence of queues, which is often overlooked in simpler models.

The second part significantly extends this foundation by integrating Batch Markovian Arrival Processes (BMAP) and non-exponential service time distributions (Erlang, Cox, and hyper-exponential laws). This allows for a more realistic characterisation of input traffic, reflective of bursty, correlated, and priority-driven request flows typical of real-world 5G-IoT systems. A range of advanced mathematical tools is introduced, including the matrix-geometric method, order statistics, and extreme value theory, enabling the derivation of upper and lower bounds for key performance indicators.

In recent decades, the evolution of digital technologies has led to the proliferation of highly distributed, dynamic, and heterogeneous information and communication systems (ICS). Among them, 5G-IoT infrastructures, with their inherently dense topologies, variable load patterns, and heightened vulnerability to cyber threats, have introduced new challenges in ensuring resource efficiency, operational continuity, and dependable performance. These systems increasingly underpin critical domains such as – Industrial automation, smart energy, transportation, and healthcare–where failures or delays are intolerable. Consequently, the demand for robust analytical models capable of capturing the complexity of such infrastructures has never been greater.This monograph emerged from the recognition that conventional queuing theory frameworks, while effective in classical settings, often fall short when applied to multi-channel systems with synchronisation mechanisms, heterogeneous service policies, and non-Poissonian traffic flows–features that are now typical in modern ICS architectures. To address this gap, the work explores and generalises a class of systems inspired by the “divide and conquer” (DC) paradigm. By formalising DC-like queuing systems, in which incoming requests are decomposed into multiple subrequests, processed in parallel, and synchronised upon completion, the study provides a coherent mathematical foundation for analysing their behaviour under both nominal and stress conditions.The first part of the monograph establishes the conceptual and mathematical core of DC-like systems, including well-known configurations such as Split-Merge (SM), Fission-Fusion (FF), Team Service Models (TSM), and Independent Server Models (ISM). These systems are studied through rigorous formulations using two-dimensional Markov chains, exact generating functions, and the evaluation of response time and synchronisation delay. In particular, special attention is given to the interdependence of queues, which is often overlooked in simpler models.The second part significantly extends this foundation by integrating Batch Markovian Arrival Processes (BMAP) and non-exponential service time distributions (Erlang, Cox, and hyper-exponential laws). This allows for a more realistic characterisation of input traffic, reflective of bursty, correlated, and priority-driven request flows typical of real-world 5G-IoT systems. A range of advanced mathematical tools is introduced, including the matrix-geometric method, order statistics, and extreme value theory, enabling the derivation of upper and lower bounds for key performance indicators.

Formal Models of Divide-and-Conquer Queuing Systems in 5G Infrastructures

International Series in Operations Research & Management Science

This book presents advanced mathematical programming models for contemporary supply chain risk optimization problems. It focuses on cutting‑edge issues such as supply chain viability under long‑term disruption risks, including reshoring decisions; optimization of digital supply chain cybersecurity and physical resilience in cyber‑physical supply chains; and supply chain fairness with respect to cost–service trade‑offs, profit distribution, and the allocation of cybersecurity investments. It also introduces computationally efficient decision‑making frameworks for emerging challenges in global supply chain management, including the reduction of cyber and physical risks and the enhancement of resilience and fairness. Each chapter ends with a table summarizing major managerial insights and the end-of-chapter problems to help the reader a self-check of material comprehension and to encourage for a further self-study.The book is intended for practitioners, researchers, and master’s and PhD‑level students in supply chain management, industrial engineering, operations research, applied mathematics, and related fields. Only an introductory background in operations research and supply chain management is assumed.

This book presents advanced mathematical programming models for contemporary supply chain risk optimization problems. It focuses on cutting‑edge issues such as supply chain viability under long‑term disruption risks, including reshoring decisions; optimization of digital supply chain cybersecurity and physical resilience in cyber‑physical supply chains; and supply chain fairness with respect to cost–service trade‑offs, profit distribution, and the allocation of cybersecurity investments. It also introduces computationally efficient decision‑making frameworks for emerging challenges in global supply chain management, including the reduction of cyber and physical risks and the enhancement of resilience and fairness. Each chapter ends with a table summarizing major managerial insights and the end-of-chapter problems to help the reader a self-check of material comprehension and to encourage for a further self-study. The book is intended for practitioners, researchers, and master’s and PhD‑level students in supply chain management, industrial engineering, operations research, applied mathematics, and related fields. Only an introductory background in operations research and supply chain management is assumed.

Viability, Cybersecurity, and Fairness

Optimization Models for Supply Chains

Classroom Companion: Business

This open-access guide offers a practical, step-by-step introduction to partial least squares structural equation modeling (PLS-SEM) using R and the SEMinR package. PLS-SEM is widely used for analyzing complex relationships between observed and latent variables, valued for its flexibility, predictive capabilities, and ability to handle complex models.Now in its second edition, the book incorporates the latest research, enhanced model evaluation routines, and updated guidance on methodological choices and result interpretation. Each chapter combines a concise overview of key concepts, metrics, and rules of thumb with a detailed case study to support hands-on learning.Designed for researchers and practitioners alike, this guide provides the essential tools and insights needed to apply PLS-SEM effectively using SEMinR.

A Workbook

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

Logistic regression is one of the most widely used tools in statistical modelling, yet the gap between textbook theory and real-world practice remains a persistent challenge for students, researchers, and practitioners alike. This book bridges that gap with a comprehensive, hands-on guide to binary outcome modelling that goes well beyond the basics.Built on a foundation of rigorous statistical theory, the book tackles the messy realities that applied analysts routinely face, including separation, rare events bias, overdispersion, and multicollinearity, offering clear and practical strategies for each. Rather than treating these as edge cases, the author positions them as central concerns deserving serious methodological attention. Modern variable selection strategies are examined in depth, contrasting traditional approaches with contemporary regularisation methods, while advanced topics such as Bayesian logistic regression and propensity score methods broaden the reader's analytical toolkit.Throughout, statistical theory is integrated with computational methods and domain knowledge, grounded in reproducible R code, simulated examples, and real-world applications drawn from fields where the stakes of getting it wrong are high.Whether you are an advanced undergraduate or graduate student studying regression modelling or applied statistics, a researcher navigating imbalanced outcomes in epidemiology or finance, or a data scientist seeking reliable methods for classification problems, this book offers the depth and practicality to meet you where you are and take your work further.

Bridging Theory and Practice

Logistic Regression

The global advertising market is roughly US$1.1 trillion, with digital channels accounting for most of that activity. Marketing measurement therefore increasingly depends on complex data environments: high-dimensional covariates, machine-learning systems, continuous treatments, platform reporting constraints, and organisational pressure to turn evidence into decisions. These settings create opportunities for richer causal analysis, but they also raise difficult questions about validity, uncertainty, diagnostics, reproducibility, and whether an estimated effect is useful for the decision at hand.Volume 2 of Causal Inference in Marketing: A Practical Toolkit for Panel Data carries the framework of Volume 1 into the advanced and operational half of the book. It extends the core panel toolkit into machine learning, high-dimensional adjustment, continuous and nonlinear treatment settings, threats to validity, inference, diagnostics, applied marketing workflows, data and measurement systems, reproducibility, and open problems. The emphasis throughout is on applying causal principles under the constraints of real marketing data and real organisational settings.Key Features:<ul> <li>Develops machine-learning and high-dimensional methods for panel data, including orthogonalisation, cross-fitting under panel dependence, heterogeneous treatment effects, policy learning, regularisation, and double selection.</li> <li>Provides a diagnostics and inference playbook covering pre-trends, placebos, sensitivity analysis, bootstrap and randomisation inference, multiplicity, weak instruments, and uncertainty communication.</li> <li>Connects advanced causal methods to marketing applications, including media mix models, geo-experiments, platform data, pricing, promotions, customer lifetime value, retention, measurement systems, and reproducible evidence production.</li> </ul>Written for data scientists, marketing analysts, econometricians, and applied researchers, this volume is intended for readers who are comfortable with regression and applied statistics and who want to extend causal design into robust implementation, diagnosis, and reporting. Volume 1 develops the foundations, including potential outcomes, design-based thinking, difference-in-differences, event studies, synthetic control, factor and matrix methods, dynamics, heterogeneity, interference, and spillovers.Charles Shaw is a Data Science Director at WPP Media, where he leads econometric measurement and optimisation for global brands. His work focuses on causal inference, econometric measurement, Bayesian modelling, machine learning, and marketing effectiveness. He develops applied frameworks for privacy-constrained attribution, media incrementality, platform effects, dynamic pricing, and scalable causal workflows in commercial settings.

Machine Learning, Diagnostics, Applications, and Outlook, Volume 2

Causal Inference in Marketing: A Practical Toolkit for Panel Data

Statistik gilt im wirtschaftswissenschaftlichen Studium oft als trockene Hürde voller abstrakter Formeln. Dieses Lehrbuch zeigt, dass es auch anders geht: Es setzt auf einen anwendungsorientierten Zugang, der Statistik als praktisches Werkzeug begreift – zur Datenanalyse, Entscheidungsfindung und zum Erkennen von Zusammenhängen.
Statt theoretischer Herleitungen stehen konkrete Rechenwege im Mittelpunkt, erklärt wie in einem Rezept: Was wird gebraucht, wie wird vorgegangen, was kommt dabei heraus? Formeln werden Schritt für Schritt eingesetzt und anhand nachvollziehbarer Beispiele aus Wirtschaft, Nachhaltigkeit, Tourismus und Sport erklärt – ganz ohne Kugeln und Urnen.
Jedes Kapitel enthält zahlreiche Übungsaufgaben mit ausführlich kommentierten Lösungen, die jeden Zwischenschritt transparent machen. Die behandelten Themen decken alle Grundlagen ab, die in ein- oder zweisemestrigen Statistikmodulen typischerweise verlangt werden: von beschreibender Statistik über Wahrscheinlichkeitsrechnung und Verteilungen bis hin zu Schätzverfahren, Hypothesentests und Regressionsanalysen.
Der Inhalt
<ul>
<li>Statistische Grundbegriffe</li>
<li>Lage- und Streuungsparameter</li>
<li>Einfache Regressionsanalyse</li>
<li>Diskrete und stetige Zufallsvariablen und Verteilungen</li>
<li>Stichprobenverteilungen und Schätztheorie</li>
<li>Multiple Regression</li>
</ul>
Die Autorin Dr. rer. pol. Stefanie Flotho ist Diplom-Mathematikerin und seit vielen Jahren an verschiedenen Hochschulen in Süddeutschland, im Elsass und der Schweiz als Dozentin für quantitative Methoden und Volkswirtschaftslehre tätig. Ihre Lehrveranstaltungen werden regelmäßig sehr gut evaluiert und mit Preisen ausgezeichnet.

Einfach und verständlich

Wirtschaftsstatistik

Dynamic Modeling and Econometrics in Economics and Finance

Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data.
Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts progressively, making it suitable for both intermediate and advanced readers. Each chapter is written in clear, approachable language, ensuring that even those with limited prior experience in econometrics can grasp and apply the material effectively.
The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis.

A Practical Approach

Non-Linearity in Econometric Modeling, Vol. 1

Wissen-KOMPAKT / Auf einen Blick!

STARK Mathematik - KOMPAKT - Basiswissen 5.-10. Klasse

Probability theory provides the foundation for understanding uncertainty in fields ranging from finance and insurance to engineering and data science. This textbook is designed for undergraduate actuarial students seeking a clear, practical, and mathematically sound introduction to the subject. Requiring only a first course in calculus, it develops core ideas in a structured and accessible way, making it suitable for a one-semester course as well as for independent study.What sets this book apart is its balanced approach between intuition and rigor. Rather than relying heavily on measure-theoretic formalism or focusing solely on computational techniques, it introduces essential concepts-such as probability spaces-in a gradual and approachable manner. By combining theoretical development with computational tools, the text bridges the gap between abstract understanding and real-world application, preparing students for both academic study and professional practice.Key features:<ul> <li>A balanced presentation that introduces foundational concepts of probability without heavy measure-theoretic machinery</li> <li>Clear, step-by-step development starting from set theory and building toward probability models and applications</li> <li>Numerous practice questions adapted from Society of Actuaries Probability (P) exam materials</li> <li>Selected solutions with detailed explanations to support self-study and deeper understanding</li> <li>Integrated Python simulations in every section to illustrate concepts and verify results</li> <li>Accompanying Jupyter notebooks available online for interactive learning and experimentation</li> </ul>By combining mathematical rigor, practical exercises, and modern computational tools, this book offers a solid introduction to probability theory. It is ideal for actuarial students preparing for professional exams, while also serving as a valuable resource for students in related fields who seek a solid and engaging foundation in probability.

Theory, Simulation, and Actuarial Applications

Computational Probability with Python

Two-Tier Stochastic Frontier Analysis for the Social Sciences

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