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Titlebook: Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis; Essays in Honor of H Xiaohong Chen,Norman R. Sw

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发表于 2025-3-21 17:14:24 | 显示全部楼层 |阅读模式
书目名称Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
副标题Essays in Honor of H
编辑Xiaohong Chen,Norman R. Swanson
视频video
概述Contains previously unpublished chapters written by the foremost academics in their respective areas of theoretical, methodological, and applied econometrics.Presents new theoretical results on estima
图书封面Titlebook: Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis; Essays in Honor of H Xiaohong Chen,Norman R. Sw
描述.This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index m
出版日期Book 2013
版次1
doihttps://doi.org/10.1007/978-1-4614-1653-1
isbn_softcover978-1-4899-9971-9
isbn_ebook978-1-4614-1653-1
copyrightSpringer Science+Business Media New York 2013
The information of publication is updating

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Smooth Constrained Frontier Analysis,estimators that may be particularly appealing to practitioners who require smooth (i.e., continuously differentiable) estimates that, in addition, are consistent with theoretical axioms of production.
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Improving U.S. GDP Measurement: A Forecast Combination Perspective,.. We propose and explore a “forecast combination” approach to combining them. We then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDP.. We compare GDP. to GDP. and GDP., with particular attention to behavior over the business cycle. We discuss several
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Identification Without Exogeneity Under Equiconfounding in Linear Recursive Structural Systems,ariables are exogenous or conditionally exogenous. In particular, standard instrumental variables and control variables need not be available in these systems. Instead, we demonstrate that the availability of one or two variables that are equally affected by the unobserved confounder as is the respo
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Optimizing Robust Conditional Moment Tests: An Estimating Function Approach,he optimality of these RCM tests by reinterpreting them as score tests under certain full specifications. This argument is in fact incompatible with the rationale of RCM tests. In this study, we consider a generalized RCM test based on the estimating function (EF) approach and explore a semi-paramet
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Asymptotic Properties of Penalized M Estimators with Time Series Observations,operties of penalized M estimators with weakly dependent data. We first establish the convergence rate for any penalized M estimators of unknown functions with stationary beta mixing observations. While the existing theories on the convergence rates with i.i.d. data require that the random criteria
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New Directions in Information Matrix Testing: Eigenspectrum Tests,5, 1982) proposed that model misspecification could be detected by testing the null hypothesis that the Fisher information matrix (IM) Equality holds by comparing linear functions of the Hessian to outer product gradient (OPG) inverse covariance matrix estimators. Unfortunately, a number of research
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