Exploring the History of Statistical Inference in Economics PDF Download
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Author: Jeff E. Biddle
Publisher:
ISBN: 9781478017356
Category :
Languages : en
Pages : 332
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Book Description
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: Jeff E. Biddle
Publisher:
ISBN: 9781478017356
Category :
Languages : en
Pages : 332
Get Book Here
Book Description
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: Deborah G. Mayo
Publisher: Cambridge University Press
ISBN: 1108563309
Category : Mathematics
Languages : en
Pages : 503
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Book Description
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: H. Spencer Banzhaf
Publisher: Cambridge University Press
ISBN: 1108491006
Category : Business & Economics
Languages : en
Pages : 299
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Book Description
This book tells how economics shifted from developing resources to valuing and incentivizing the preservation of natural environments.
Author: Eglė Rindzevičiūtė
Publisher: Cornell University Press
ISBN: 1501769790
Category : History
Languages : en
Pages : 188
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Book Description
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: Aris Spanos
Publisher: Cambridge University Press
ISBN: 1107185149
Category : Business & Economics
Languages : en
Pages : 787
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Book Description
This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.
Author: Anatoli Juditsky
Publisher: Princeton University Press
ISBN: 0691197296
Category : Mathematics
Languages : en
Pages : 655
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Book Description
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: Bent Jesper Christensen
Publisher: Princeton University Press
ISBN: 9780691120591
Category : Business & Economics
Languages : en
Pages : 508
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Book Description
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: Miltiadis C. Mavrakakis
Publisher: CRC Press
ISBN: 131536204X
Category : Mathematics
Languages : en
Pages : 444
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Book Description
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: John R. Meyer
Publisher: Routledge
ISBN: 1351304429
Category : Business & Economics
Languages : en
Pages : 276
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Book Description
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.
Author: Randall E. Schumacker
Publisher: SAGE Publications
ISBN: 148332477X
Category : Social Science
Languages : en
Pages : 648
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Book Description
Providing easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Randall E. Schumacker’s comprehensive book describes in detail the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Learning Statistics Using R covers data input from vectors, arrays, matrices and data frames, as well as the input of data sets from SPSS, SAS, STATA and other software packages. Schumacker’s text provides the freedom to effectively calculate, manipulate, and graphically display data, using R, on different computer operating systems without the expense of commercial software. Learning Statistics Using R places statistics within the framework of conducting research, where statistical research hypotheses can be directly addressed. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers′ understanding of statistics through statistical computing and modeling.
