Robustness In Econometrics
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Robustness In Econometrics
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Author : Vladik Kreinovich
language : en
Publisher: Springer
Release Date : 2017-02-11
Robustness In Econometrics written by Vladik Kreinovich and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-11 with Technology & Engineering categories.
This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.
Robustness
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Author : Lars Peter Hansen
language : en
Publisher: Princeton University Press
Release Date : 2016-06-28
Robustness written by Lars Peter Hansen and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-28 with Business & Economics categories.
The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.
Robustness Tests For Quantitative Research
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Author : Eric Neumayer
language : en
Publisher: Cambridge University Press
Release Date : 2017-08-17
Robustness Tests For Quantitative Research written by Eric Neumayer and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-17 with Business & Economics categories.
This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.
Heavy Tailed Distributions And Robustness In Economics And Finance
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Author : Marat Ibragimov
language : en
Publisher:
Release Date : 2015
Heavy Tailed Distributions And Robustness In Economics And Finance written by Marat Ibragimov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
This book focuses on general frameworks for modeling heavy-tailed distributions in economics, finance, econometrics, statistics, risk management and insurance. A central theme is that of (non-)robustness, i.e., the fact that the presence of heavy tails can either reinforce or reverse the implications of a number of models in these fields, depending on the degree of heavy-tailedness. These results motivate the development and applications of robust inference approaches under heavy tails, heterogeneity and dependence in observations. Several recently developed robust inference approaches are discussed and illustrated, together with applications.
On The Robustness Of Lm Lr And W Tests In Regression Models
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Author : Ullah, A. (Aman)
language : en
Publisher: London : Department of Economics, University of Western Ontario
Release Date : 1983
On The Robustness Of Lm Lr And W Tests In Regression Models written by Ullah, A. (Aman) and has been published by London : Department of Economics, University of Western Ontario this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Econometrics categories.
Robustness Against Incidental Parameters And Mixing Distributions
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Author : Tiemen Woutersen
language : en
Publisher: London : Department of Economics, University of Western Ontario
Release Date : 2001
Robustness Against Incidental Parameters And Mixing Distributions written by Tiemen Woutersen and has been published by London : Department of Economics, University of Western Ontario this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Robustness Against Incidental Parameters
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Author : Tiemen Woutersen
language : en
Publisher:
Release Date : 2002
Robustness Against Incidental Parameters written by Tiemen Woutersen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Robust Methods And Asymptotic Theory In Nonlinear Econometrics
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Author : H. J. Bierens
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Robust Methods And Asymptotic Theory In Nonlinear Econometrics written by H. J. Bierens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.
The Robustness Of Estimators For Dynamic Panel Data Models To Misspecification
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Author : Mark Norman Harris
language : en
Publisher:
Release Date : 1996
The Robustness Of Estimators For Dynamic Panel Data Models To Misspecification written by Mark Norman Harris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Econometrics categories.
Developments In Robust Statistics
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Author : Rudolf Dutter
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Developments In Robust Statistics written by Rudolf Dutter and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Aspects of Robust Statistics are important in many areas. Based on the International Conference on Robust Statistics 2001 (ICORS 2001) in Vorau, Austria, this volume discusses future directions of the discipline, bringing together leading scientists, experienced researchers and practitioners, as well as younger researchers. The papers cover a multitude of different aspects of Robust Statistics. For instance, the fundamental problem of data summary (weights of evidence) is considered and its robustness properties are studied. Further theoretical subjects include e.g.: robust methods for skewness, time series, longitudinal data, multivariate methods, and tests. Some papers deal with computational aspects and algorithms. Finally, the aspects of application and programming tools complete the volume.