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Author: George G. Judge
Publisher: North Holland
ISBN:
Category : Business & Economics
Languages : en
Pages : 316
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Book Description
This book has as its general objective the improvement of estimation rules for linear statistical models and the development of estimating procedures, to be used with a single data set, that are appropriate to economic decision problems. Advances in the estimating procedure are brought about by changing: (i) the statistical model, (ii) the amount of information used, and (iii) the measure of performance. Within this context the book considers estimation and hypothesis testing when sample information and non-sample information of an inequality form are combined. Also evaluated are: the statistical consequences of using traditional and non-traditional estimators when the error assumptions are weakened; and the precision and statistical implications of new Stein estimators.
Author: George G. Judge
Publisher: North Holland
ISBN:
Category : Business & Economics
Languages : en
Pages : 316
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Book Description
This book has as its general objective the improvement of estimation rules for linear statistical models and the development of estimating procedures, to be used with a single data set, that are appropriate to economic decision problems. Advances in the estimating procedure are brought about by changing: (i) the statistical model, (ii) the amount of information used, and (iii) the measure of performance. Within this context the book considers estimation and hypothesis testing when sample information and non-sample information of an inequality form are combined. Also evaluated are: the statistical consequences of using traditional and non-traditional estimators when the error assumptions are weakened; and the precision and statistical implications of new Stein estimators.
Author: Stanislav Anatolyev
Publisher: CRC Press
ISBN: 1439838267
Category : Business & Economics
Languages : en
Pages : 230
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Book Description
This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.
Author: George G. Judge
Publisher: North Holland
ISBN:
Category : Business & Economics
Languages : en
Pages : 316
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Book Description
This book has as its general objective the improvement of estimation rules for linear statistical models and the development of estimating procedures, to be used with a single data set, that are appropriate to economic decision problems. Advances in the estimating procedure are brought about by changing: (i) the statistical model, (ii) the amount of information used, and (iii) the measure of performance. Within this context the book considers estimation and hypothesis testing when sample information and non-sample information of an inequality form are combined. Also evaluated are: the statistical consequences of using traditional and non-traditional estimators when the error assumptions are weakened; and the precision and statistical implications of new Stein estimators.
Author: Roberto Mariano
Publisher: Cambridge University Press
ISBN: 9780521591126
Category : Business & Economics
Languages : en
Pages : 488
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Book Description
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
Author: Richard J. Murnane
Publisher: Oxford University Press
ISBN: 0199890153
Category : Psychology
Languages : en
Pages : 414
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Book Description
Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.
Author: Heng Shu
Publisher:
ISBN:
Category :
Languages : en
Pages : 118
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Book Description
In this dissertation, we develop improved estimation of average treatment effect on the treatment (ATT) which achieves double robustness, local efficiency, intrinsic efficiency and sample boundedness, using a calibrated likelihood approach. Moreover, we consider an extension of two-group causal inference problem to a general data combination problem, and develop estimators achieving desirable properties beyond double robustness and local efficiency. The proposed methods are shown, both theoretically and numerically, to be superior in robustness, efficiency or both to various existing estimators. In the first part, we review existing estimators on average treatment effect (ATE), mainly based on Tan (2006, 2010). This review provides a useful basis for improved estimation of average treatment effect on the treated (ATT). In the second part, we propose new methods to estimate the average treatment effect on the treated (ATT), which is of extensive interest in Econometrics, Biostatistics and other research fields. This problem seems to be often treated as a simple modification or extension of that of estimating overall average treatment effects (ATE). But the propensity score is no longer ancillary for estimation of ATT, in contrast with estimation of ATE. We study the efficient influence function and the corresponding semiparametric variance bound for the estimation of ATT under three different assumptions: a nonparametric model, a correct propensity score model and known propensity score. Then we construct Augmented Inverse Probability Weighted (AIPW) estimators which are locally efficient and doubly robust. Furthermore, we develop calibrated regression and likelihood estimators that are not only doubly robust and locally efficient, but also intrinsically e cient and sample bounded. Two simulations and real data analysis on a job training program are provided to demonstrate the advantage of our estimators compared with existing estimators. In the third part, we extend our methods to a general data combination problem for moment restriction models (Chen et al. 2008). Similarly, we derive augmented inverse probability weighted (AIPW) estimators that are locally efficient and doubly robust. Moreover, we develop calibrated regression and likelihood estimators which achieve double robustness, local efficiency and intrinsic efficiency. For illustration, we take the linear two-sample instrumental variable problem as an example, and derive all the relevant estimators by applying the general estimators in this specific example. Finally, a simulation study and an Econometric application on a public housing project are provided to demonstrate the superior performance of our improved estimators.
Author: Aman Ullah
Publisher: CRC Press
ISBN: 9780203911075
Category : Business & Economics
Languages : en
Pages : 754
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Book Description
Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering, sociology, psychology, and information technology. It supplies a geometric proof of an extended Gauss-Markov theorem, approaches for the design and implementation of sample surveys, advances in the theory of Neyman's smooth test, and methods for pre-test and biased estimation. It includes discussions ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.
Author: Felix Chan
Publisher: Springer Nature
ISBN: 3031151496
Category : Business & Economics
Languages : en
Pages : 385
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Book Description
This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Author: Nikolay Gospdinov
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Methods for Estimation and Inference in Modern Econometrics.
Author: J. D. A. Cuddy
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 200
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Book Description
