Partial Identification Of Probability Distributions
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Partial Identification of Probability Distributions
Author: Charles F. Manski
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-04-29
Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event – a parameter is either identified or not – and to view point identification as a precondition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric.
Partial Identification in Econometrics and Related Topics
This book covers data processing techniques, with economic and financial application being the unifying theme. To make proper investments in economy, the authors need to have a good understanding of the future trends: how will demand change, how will prices change, etc. In general, in science, the usual way to make predictions is: to identify a model that best fits the current dynamics, and to use this model to predict the future behavior. In many practical situations—especially in economics—our past experiences are limited. As a result, the authors can only achieve a partial identification. It is therefore important to be able to make predictions based on such partially identified models—which is the main focus of this book. This book emphasizes partial identification techniques, but it also describes and uses other econometric techniques, ranging from more traditional statistical techniques to more innovative ones such as game-theoretic approach, interval techniques, and machine learning. Applications range from general analysis of GDP growth, stock market, and consumer prices to analysis of specific sectors of economics (credit and banking, energy, health, labor, tourism, international trade) to specific issues affecting economy such as ecology, national culture, government regulations, and the existence of shadow economy. This book shows what has been achieved, but even more important are remaining open problems. The authors hope that this book will: inspire practitioners to learn how to apply state-of-the-art techniques, especially techniques of optimal transport statistics, to economic and financial problems, and inspire researchers to further improve the existing techniques and to come up with new techniques for studying economic and financial phenomena. The authors want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments. The publication of this book—and organization of the conference at which these papers were presented—was supported: by the Ho Chi Minh University of Banking (HUB), Vietnam, and by the Vingroup Innovation Foundation (VINIF). The authors thank the leadership and staff of HUB and VINIF for providing crucial support.
Handbook of Econometrics
Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. - Presents a broader and more comprehensive view of this expanding field than any other handbook - Emphasizes the connection between econometrics and economics - Highlights current topics for which no good summaries exist