Exploring The History Of Statistical Inference In Economics
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| Author | : Jeff E. Biddle |
| Publisher | : |
| Total Pages | : 332 |
| Release | : 2021-12-10 |
| Genre | : |
| ISBN | : 9781478017356 |
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.
| Author | : Nitis Mukhopadhyay |
| Publisher | : CRC Press |
| Total Pages | : 289 |
| Release | : 2006-02-07 |
| Genre | : Mathematics |
| ISBN | : 1420017403 |
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.
| Author | : Deborah G. Mayo |
| Publisher | : Cambridge University Press |
| Total Pages | : 503 |
| Release | : 2018-09-20 |
| Genre | : Mathematics |
| ISBN | : 1108563309 |
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.
| Author | : Aris Spanos |
| Publisher | : Cambridge University Press |
| Total Pages | : 787 |
| Release | : 2019-09-19 |
| Genre | : Business & Economics |
| ISBN | : 1107185149 |
This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.
| Author | : Eglė Rindzevičiūtė |
| Publisher | : Cornell University Press |
| Total Pages | : 188 |
| Release | : 2023-05-15 |
| Genre | : History |
| ISBN | : 1501769790 |
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.
| Author | : Miltiadis C. Mavrakakis |
| Publisher | : CRC Press |
| Total Pages | : 444 |
| Release | : 2021-03-28 |
| Genre | : Mathematics |
| ISBN | : 131536204X |
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.
| Author | : H. Spencer Banzhaf |
| Publisher | : Cambridge University Press |
| Total Pages | : 299 |
| Release | : 2023-11-02 |
| Genre | : Business & Economics |
| ISBN | : 1108491006 |
This book tells how economics shifted from developing resources to valuing and incentivizing the preservation of natural environments.
| Author | : Bent Jesper Christensen |
| Publisher | : Princeton University Press |
| Total Pages | : 508 |
| Release | : 2009 |
| Genre | : Business & Economics |
| ISBN | : 9780691120591 |
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
| Author | : Anatoli Juditsky |
| Publisher | : Princeton University Press |
| Total Pages | : 655 |
| Release | : 2020-04-07 |
| Genre | : Mathematics |
| ISBN | : 0691197296 |
This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems—sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals—demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text.
| Author | : Michael J. Panik |
| Publisher | : John Wiley & Sons |
| Total Pages | : 294 |
| Release | : 2012-06-06 |
| Genre | : Mathematics |
| ISBN | : 1118309804 |
A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causality. To ensure a thorough understanding of all key concepts, Statistical Inference provides numerous examples and solutions along with complete and precise answers to many fundamental questions, including: How do we determine that a given dataset is actually a random sample? With what level of precision and reliability can a population sample be estimated? How are probabilities determined and are they the same thing as odds? How can we predict the level of one variable from that of another? What is the strength of the relationship between two variables? The book is organized to present fundamental statistical concepts first, with later chapters exploring more advanced topics and additional statistical tests such as Distributional Hypotheses, Multinomial Chi-Square Statistics, and the Chi-Square Distribution. Each chapter includes appendices and exercises, allowing readers to test their comprehension of the presented material. Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools.
