Econometrics With Partial Identification
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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.
Econometrics with Partial Identification
Econometrics has traditionally revolved around point identi cation. Much effort has been devoted to finding the weakest set of assumptions that, together with the available data, deliver point identifi cation of population parameters, finite or infi nite dimensional that these might be. And point identifi cation has been viewed as a necessary prerequisite for meaningful statistical inference. The research program on partial identifi cation has begun to slowly shift this focus in the early 1990s, gaining momentum over time and developing into a widely researched area of econometrics. Partial identification has forcefully established that much can be learned from the available data and assumptions imposed because of their credibility rather than their ability to yield point identifi cation. Within this paradigm, one obtains a set of values for the parameters of interest which are observationally equivalent given the available data and maintained assumptions. I refer to this set as the parameters' sharp identifi cation region. Econometrics with partial identi fication is concerned with: (1) obtaining a tractable characterization of the parameters' sharp identification region; (2) providing methods to estimate it; (3) conducting test of hypotheses and making con fidence statements about the partially identi fied parameters. Each of these goals poses challenges that differ from those faced in econometrics with point identifi cation. This chapter discusses these challenges and some of their solution. It reviews advances in partial identifi cation analysis both as applied to learning (functionals of) probability distributions that are well-defi ned in the absence of models, as well as to learning parameters that are well-defi ned only in the context of particular models. The chapter highlights a simple organizing principle: the source of the identi fication problem can often be traced to a collection of random variables that are consistent with the available data and maintained assumptions. This collection may be part of the observed data or be a model implication. In either case, it can be formalized as a random set. Random set theory is then used as a mathematical framework to unify a number of special results and produce a general methodology to conduct econometrics with partial identi fication.
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