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Asian Development Outlook: 2021
International Comparison Program for As

2021 International Comparison Program for Asia and the Pacific

Purchasing Power Parities and Real Expenditures-A Summary Report

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

A paradigm-shifting textbook that teaches graduate students in economics how to use economic models to interpret data. Offering a new pedagogical paradigm, this textbook delivers a ready-made economics course covering the general concepts that unify different approaches to bringing economic models to data. Jesse Shapiro presents core ideas in quantitative economics as an abstract, cohesive whole and introduces canonical models that can be used across a wide range of applications. This course-in-a-book exposes students to stylized applications of these models that illuminate their foundations as well as recent research articles demonstrating the models’ broad applicability. <ul><li>Provides a coherent, generalized approach to making model-based inferences in empirical economics</li><li>Introduces central concepts and canonical models in quantitative economics</li><li>Features applications of modern research topics</li><li>Suits self-paced learning and classroom use alike</li></ul>

A paradigm-shifting textbook that teaches graduate students in economics how to use economic models to interpret data. Offering a new pedagogical paradigm, this textbook delivers a ready-made economics course covering the general concepts that unify different approaches to bringing economic models to data. Jesse Shapiro presents core ideas in quantitative economics as an abstract, cohesive whole and introduces canonical models that can be used across a wide range of applications. This course-in-a-book exposes students to stylized applications of these models that illuminate their foundations as well as recent research articles demonstrating the models’ broad applicability. <ul><li>Provides a coherent, generalized approach to making model-based inferences in empirical economics</li><li>Introduces central concepts and canonical models in quantitative economics</li><li>Features applications of modern research topics</li><li>Suits self-paced learning and classroom use alike</li></ul>

Introduction to Quantitative Economics

Chapman and Hall/CRC Series on Statistics in Business and Economics

Introduction to Bayesian Econometrics: A GUIded Toolkit Using R offers a practical, conceptually clear, and computationally accessible pathway into Bayesian data analysis. Designed for readers who wish to apply Bayesian methods without necessarily investing years in programming, the book combines rigorous treatment of foundational ideas with a graphical user interface (GUI) that allows users to run Bayesian regression models in a user-friendly environment.The first part develops the mathematical foundations of Bayesian inference by presenting all derivations step-by-step. This transparent treatment of conjugate models, including posterior analysis, marginal likelihoods, and posterior predictive distributions, provides readers with a strong theoretical base for the more advanced material that follows.The second part focuses on implementation. It introduces the custom GUI for readers with little or no programming experience, demonstrates how to fit Bayesian models using established R packages, and guides more advanced users through programming key components of Bayesian samplers from scratch. This integrated approach enables readers with different backgrounds to engage with Bayesian methods at their preferred level of computational depth.The third part extends the framework to modern Bayesian econometrics. It covers Bayesian machine learning, causal inference, and approximate methods, illustrating how Bayesian ideas can be applied to contemporary empirical challenges. By combining theory, software, and hands-on computation, the book provides a comprehensive entry point into both classical and modern Bayesian analysis.Across all parts, the book is designed to support a wide range of users -beginners, intermediate programmers, and advanced learners-. To the best of the author's knowledge, no existing text combines mathematical transparency, software accessibility, and modern Bayesian topics in a single, integrated resource.

A GUIded Toolkit using R

Introduction to Bayesian Econometrics

Türkiye Application

Test of Exchange Rate Determination Models

National Bureau of Economic Research Conference Report

A comprehensive survey of the evolution of market design over the past three decades. In the mid-1990s, the first Federal Communications Commission spectrum auction and the redesign of the National Residency Matching Program collectively helped to jumpstart the field of market design. Since then, extensive research has improved auction design and broken new conceptual ground in addressing multi-agent matching problems. This volume summarizes key discoveries and advances in market design over the past three decades and explores contemporary challenges--from climate policy and electricity markets to AI-mediated exchanges and hospital resource allocation. Contributors examine how to design efficient, incentive-compatible mechanisms that are robust to shifting conditions and increasing complexity. They consider a wide variety of applications that could benefit from the market design viewpoint, such as environmental markets, school choice, and organ exchange. Together, the chapters illustrate the important interactions between economic theory, computational tools, and institutional insight.

New Directions in Market Design

Econometric Society Monographs

A Framework for Economists with Policy Aspirations

Minimalist Market Design

Derivatives: Mathematical Foundations for Finance Students is written for students of finance and actuarial science who want to understand derivatives not just as formulas but as ideas that make sense. The goal is to build the mathematical foundations up step by step - starting from the familiar territory of first-year undergraduate probability and statistics and moving all the way to stochastic calculus and the principles of risk-neutral pricing, which form the heart of modern financial mathematics.This text will enable the reader to:¿ understand models of asset price behavior and apply stochastic calculus ¿ use stochastic simulations in R to investigate processes and price options ¿ price options with the binomial model ¿ derive solutions to the Black-Scholes equation using risk-neutral pricing ¿ price interest-rate derivatives using Black's model, short-rate models and the Heath-Jarrow-Morton (HJM) framework ¿ tackle quantitative finance exam questions.It incorporates a range of learning features to aid student understanding, including boxed examples, end-of-chapter summaries, selected questions from Society of Actuaries (US) Quantitative Finance examinations and further reading suggestions. The book is also supported by a suite of digital learning resources, including PowerPoint slides, multiple choice questions, instructor manual/advice document for lecturers and a test bank.This book will appeal to both undergraduate and postgraduate students who wish to understand the principles of stochastic calculus and option pricing.

Mathematical Foundations for Finance Students

Derivatives

This book is based on a course on penalized high-dimensional estimation that the author taught for second year graduate students in economics at Northwestern University. The book concentrates on ideas, methods, and results that are useful for empirical research in economics and related fields but avoids complicated mathematics and technical discussions. It explains why the results it presents are true, provides informal derivations of them, and presents some proofs. The book treats LASSO estimation of linear models and the properties of estimates obtained with the LASSO and several other penalization methods. It also treats nonlinear and conditional moment models, endogeneity and instrumental variables estimation, quantile models, and non- and semiparametric models. The treatment of inference includes bootstrap asymptotic refinements for linear and nonlinear models. In all cases, it aims at providing understanding without the difficult mathematics that are often part of discussions of these topics. The book avoids methods and results that involve complex mathematics that are not central to the main ideas underlying penalized estimation and that non-statisticians and non-econometricians often find inaccessible. It provides references to journal articles and books that treat mathematically difficult topics.Key Features:¿ Treats ideas, methods, and results that are useful for empirical research in economics and related fields but avoids complicated mathematics that non-statisticians and non-econometricians often find inaccessible.¿ Explains why the results it presents are true, provides informal derivations of them, and presents some proofs. ¿ Treats penalized estimation of linear and nonlinear models, conditional moment models, endogeneity and instrumental variables estimation, quantile models, non- and semiparametric models, and bootstrap methods for inference. ¿ Presents computational methods.¿ Includes exercises and computational problems.

Penalized Estimation of High Dimensional Models

Air Transportation Industry considers the influence of political, legal, economic, social, and technological factors on the developments in the industry. It provides a brief historical background of the air transport industry, the determinants of the changes in the airline business, and adaptation processes that resulted in the evolution of business models and structural changes in the industry.Utilizing a unique database containing the characteristics of more than 16,000 air carriers worldwide, the book discusses key findings related to changes in the transport capacity of airlines operating at different points in time, including the length of the lifecycle and reasons for termination of activity, types, and geographical scope of operations. It also explores the impacts of global and regional-scale legal regulations.The book will interest air transportation and airport operations researchers. It can also serve as a reference for management and operations transportation students in logistics, air transportation, and economics courses.

History and Developments

Air Transportation Industry

World Scientific Lecture Notes In Economics And Policy

This book attempts to understand the operation and properties of 'market economies'. We do this by carefully examining, refining and in some cases extending the major theorems that Microeconomics has established in response to the Walrasian conjecture. We critically evaluate theorems and results, that assert the existence, uniqueness, stability, optimality and empirical relevance of Walrasian equilibrium states. As a result of our work, we attempt to comment on the wisdom of recommending that socio-economic systems operate as a collection of deregulated competitive markets.

Lectures In General Equilibrium Microeconomics

Mastering Business Analytics

Data Analytics for Strategic Decisions explores how businesses can harness the power of data, technology, and analytics to make strategic decisions that drive growth and innovation. It provides a comprehensive guide to integrating business strategy with data-driven methodologies, offering practical tools and insights for modern organizations.Readers will gain a deep understanding of how to transform data into actionable insights, improving decision-making across marketing, finance, operations, and strategy. The book combines theoretical foundations with real-world applications, including case studies, datasets, and practical exercises. It also addresses emerging topics like AI, predictive analytics, and data governance, ensuring readers are equipped with cutting-edge knowledge and skills.This book is designed for undergraduate and master's students in Business Administration, Business Analytics, and Digital Transformation, as well as professionals seeking to enhance their data-driven decision-making capabilities. It is ideal for courses in Business Analytics, Data Analytics for Business, Business Intelligence, and Digital Transformation.The book includes supplementary materials such as datasets for practical exercises, case studies, and discussion questions, making it suitable for both classroom and online learning environments.

Fundamentals of Business Technology and AI Integration

Data Analytics for Strategic Decisions

2021 International Comparison Program for Asia and the Pacific

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