Contributors to this special supplement explore the history of statistical inference, led by two motivations. One was the belief that John Maynard Keynes's distinction between the descriptive and the inductive function of statistical research provided a fruitful framework for understanding empirical research practices. The other was an aim to fill a gap in the history of economics by exploring an important part of the story left out of existing histories of empirical analysis in economics--namely "sinful" research practices that did not meet or point towards currently reigning standards of scientific research.

Introductory Statistical Inference develops the concepts and intricacies of statistical inference. With a review of probability concepts, this book discusses topics such as sufficiency, ancillarity, point estimation, minimum variance estimation, confidence intervals, multiple comparisons, and large-sample inference. It introduces techniques of two-stage sampling, fitting a straight line to data, tests of hypotheses, nonparametric methods, and the bootstrap method. It also features worked examples of statistical principles as well as exercises with hints. This text is suited for courses in probability and statistical inference at the upper-level undergraduate and graduate levels.

Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.

In The Will to Predict, Eglė Rindzevičiūtė demonstrates how the logic of scientific expertise cannot be properly understood without knowing the conceptual and institutional history of scientific prediction. She notes that predictions of future population, economic growth, environmental change, and scientific and technological innovation have shaped much of twentieth and twenty-first-century politics and social life, as well as government policies. Today, such predictions are more necessary than ever as the world undergoes dramatic environmental, political, and technological change. But, she asks, what does it mean to predict scientifically? What are the limits of scientific prediction and what are its effects on governance, institutions, and society? Her intellectual and political history of scientific prediction takes as its example twentieth-century USSR. By outlining the role of prediction in a range of governmental contexts, from economic and social planning to military strategy, she shows that the history of scientific prediction is a transnational one, part of the history of modern science and technology as well as governance. Going beyond the Soviet case, Rindzevičiūtė argues that scientific predictions are central for organizing uncertainty through the orchestration of knowledge and action. Bridging the fields of political sociology, organization studies, and history, The Will to Predict considers what makes knowledge scientific and how such knowledge has impacted late modern governance.

Probability and Statistical Inference: From Basic Principles to Advanced Models covers aspects of probability, distribution theory, and inference that are fundamental to a proper understanding of data analysis and statistical modelling. It presents these topics in an accessible manner without sacrificing mathematical rigour, bridging the gap between the many excellent introductory books and the more advanced, graduate-level texts. The book introduces and explores techniques that are relevant to modern practitioners, while being respectful to the history of statistical inference. It seeks to provide a thorough grounding in both the theory and application of statistics, with even the more abstract parts placed in the context of a practical setting. Features: •Complete introduction to mathematical probability, random variables, and distribution theory. •Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes. •Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference. •Detailed introduction to Bayesian statistics and associated topics. •Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC). This book is for students who have already completed a first course in probability and statistics, and now wish to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our aim is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and approachable than those in advanced texts. This book is also useful for data scientists, researchers, and other applied practitioners who want to understand the theory behind the statistical methods used in their fields.

Economic Modeling and Inference takes econometrics to a new level by demonstrating how to combine modern economic theory with the latest statistical inference methods to get the most out of economic data. This graduate-level textbook draws applications from both microeconomics and macroeconomics, paying special attention to financial and labor economics, with an emphasis throughout on what observations can tell us about stochastic dynamic models of rational optimizing behavior and equilibrium. Bent Jesper Christensen and Nicholas Kiefer show how parameters often thought estimable in applications are not identified even in simple dynamic programming models, and they investigate the roles of extensions, including measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed in the empirical procedures, the resulting estimation problems are often nonstandard, with the estimators exhibiting nonregular asymptotic behavior such as short-ranked covariance, superconsistency, and non-Gaussianity. Christensen and Kiefer explore these properties in detail, covering areas including job search models of the labor market, asset pricing, option pricing, marketing, and retirement planning. Ideal for researchers and practitioners as well as students, Economic Modeling and Inference uses real-world data to illustrate how to derive the best results using a combination of theory and cutting-edge econometric techniques. Covers identification and estimation of dynamic programming models Treats sources of error--measurement error, random utility, and imperfect control Features financial applications including asset pricing, option pricing, and optimal hedging Describes labor applications including job search, equilibrium search, and retirement Illustrates the wide applicability of the approach using micro, macro, and marketing examples

This book tells how economics shifted from developing resources to valuing and incentivizing the preservation of natural environments.

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

How are economists and historians to explain what happened in history? What statistical inferences can be drawn from historical data? The authors believe that explanation in history can be identified with the problems of prediction in a probabilistic universe. Using this approach, the historian can act upon his a priori information and his judgment of what is unique and particular in each past event, even with data hitherto considered to be intractable for statistical treatment. In essence, the book is an argument for and a demonstration of the point of view that the restricted approach of "measurement without theory" is not necessary in history, or at least not necessary in economic history. After two chapters of theoretical introduction, the authors explore the meanings and implications of evidence, explanation and proof in history by applying econometric methods to the analysis of three major problems in 19th century economic history--the profitability of slavery in the antebellum South, income growth and development in the United States during the 1800's, and The Great Depression in the British economy; also included is a postscript on growth reassessing some current arguments in the light of the findings of these papers. The book presents an original and provocative approach to historical problems that have long plagued economists and historians and provides the reader with a new approach to these and similar questions.