Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
Essays in Honor of Halbert L. White Jr
Editors: Chen, Xiaohong, Swanson, Norman R. (Eds.)
Free Preview- Contains previously unpublished chapters written by the foremost academics in their respective areas of theoretical, methodological, and applied econometrics
- Presents new theoretical results on estimation and inference by including careful development and discussion of new empirical methodology, worked empirical examples, and carefully carried out Monte Carlo experiments
- Pays particular emphasis on carefully outlining how to empirically implement all of the latest advances in econometrics when the objective is to build economic models for the purpose of prediction, causality, and policy analysis
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- About this book
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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 models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
- Table of contents (20 chapters)
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Improving U.S. GDP Measurement: A Forecast Combination Perspective
Pages 1-25
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Identification Without Exogeneity Under Equiconfounding in Linear Recursive Structural Systems
Pages 27-55
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Optimizing Robust Conditional Moment Tests: An Estimating Function Approach
Pages 57-95
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Asymptotic Properties of Penalized M Estimators with Time Series Observations
Pages 97-120
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A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance
Pages 121-143
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Table of contents (20 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
- Book Subtitle
- Essays in Honor of Halbert L. White Jr
- Editors
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- Xiaohong Chen
- Norman R. Swanson
- Copyright
- 2013
- Publisher
- Springer-Verlag New York
- Copyright Holder
- Springer Science+Business Media New York
- eBook ISBN
- 978-1-4614-1653-1
- DOI
- 10.1007/978-1-4614-1653-1
- Hardcover ISBN
- 978-1-4614-1652-4
- Softcover ISBN
- 978-1-4899-9971-9
- Edition Number
- 1
- Number of Pages
- XXXIV, 562
- Topics
