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The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. There is an enormous scope for fruitful inference using data and assumptions that partially identify population parameters.
Book Synopsis Partial Identification of Probability Distributions by : Charles F. Manski
Download or read book Partial Identification of Probability Distributions written by Charles F. Manski and published by Springer Science & Business Media. This book was released on 2006-04-29 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. There is an enormous scope for fruitful inference using data and assumptions that partially identify population parameters.
Book Synopsis Identification of Discrete Probability Distributions from Partial Information by : Michael Anthony Pittarelli
Download or read book Identification of Discrete Probability Distributions from Partial Information written by Michael Anthony Pittarelli and published by . This book was released on 1987 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification of discrete probability distributions from partial information by : Michael A. Pittarelli
Download or read book Identification of discrete probability distributions from partial information written by Michael A. Pittarelli and published by . This book was released on 1990 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Book Synopsis Microeconometrics by : Steven Durlauf
Download or read book Microeconometrics written by Steven Durlauf and published by Springer. This book was released on 2016-06-07 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement, with much debate about whether some covariate is or is not a "valid instrument" in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. assumptions. To this end, we introduce monotone instrumental variable (MIV) A particularly interesting special case of an MIV assumption is monotone treatment selection (MTS). IV and MIV assumptions may be imposed alone or in combination with other assumptions. We study the identifying power of MIV assumptions in three informational settings: MIV alone; MIV combined with the classical linear response assumption; MIV combined with the monotone treatment response (MTR) assumption. We apply the results to the problem of inference on the returns to schooling. We analyze wage data reported by white male respondents to the National Longitudinal Survey of Youth (NLSY) and use the respondent's AFQT score as an MIV. We find that this MIV assumption has little identifying power when imposed alone. However combining the MIV assumption with the MTR and MTS assumptions yields fairly tight bounds on two distinct measures of the returns to schooling.
Book Synopsis Monotone Instrumental Variables with an Application to the Returns to Schooling by : Charles F. Manski
Download or read book Monotone Instrumental Variables with an Application to the Returns to Schooling written by Charles F. Manski and published by . This book was released on 1999 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement, with much debate about whether some covariate is or is not a "valid instrument" in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. assumptions. To this end, we introduce monotone instrumental variable (MIV) A particularly interesting special case of an MIV assumption is monotone treatment selection (MTS). IV and MIV assumptions may be imposed alone or in combination with other assumptions. We study the identifying power of MIV assumptions in three informational settings: MIV alone; MIV combined with the classical linear response assumption; MIV combined with the monotone treatment response (MTR) assumption. We apply the results to the problem of inference on the returns to schooling. We analyze wage data reported by white male respondents to the National Longitudinal Survey of Youth (NLSY) and use the respondent's AFQT score as an MIV. We find that this MIV assumption has little identifying power when imposed alone. However combining the MIV assumption with the MTR and MTS assumptions yields fairly tight bounds on two distinct measures of the returns to schooling.
The author draws on examples from a range of disciplines to provide social and behavioural scientists with a toolkit for finding bounds when predicting behaviours based upon nonexperimental and experimental data.
Book Synopsis Identification Problems in the Social Sciences by : Charles F. Manski
Download or read book Identification Problems in the Social Sciences written by Charles F. Manski and published by Harvard University Press. This book was released on 1995 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author draws on examples from a range of disciplines to provide social and behavioural scientists with a toolkit for finding bounds when predicting behaviours based upon nonexperimental and experimental data.
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.
Book Synopsis Econometrics with Partial Identification by : Francesca Molinari
Download or read book Econometrics with Partial Identification written by Francesca Molinari and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Partial Identification in Econometrics and Related Topics by : Nguyen Ngoc Thach
Download or read book Partial Identification in Econometrics and Related Topics written by Nguyen Ngoc Thach and published by Springer Nature. This book was released on with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt:
This is the first systematic exposition of random sets theory since Matheron (1975), with full proofs, exhaustive bibliographies and literature notes Interdisciplinary connections and applications of random sets are emphasized throughout the book An extensive bibliography in the book is available on the Web at http://liinwww.ira.uka.de/bibliography/math/random.closed.sets.html, and is accompanied by a search engine
Book Synopsis Theory of Random Sets by : Ilya Molchanov
Download or read book Theory of Random Sets written by Ilya Molchanov and published by Springer Science & Business Media. This book was released on 2005-05-11 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first systematic exposition of random sets theory since Matheron (1975), with full proofs, exhaustive bibliographies and literature notes Interdisciplinary connections and applications of random sets are emphasized throughout the book An extensive bibliography in the book is available on the Web at http://liinwww.ira.uka.de/bibliography/math/random.closed.sets.html, and is accompanied by a search engine
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Book Synopsis High-Dimensional Probability by : Roman Vershynin
Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
