Bootstrap Inference In Time Series Econometrics

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Bootstrap Inference In Time Series Econometrics

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Author : Mikael Gredenhoff
language : en
Publisher: Stockholm School of Economics Efi Economic Research Institut
Release Date : 1998

Bootstrap Inference In Time Series Econometrics written by Mikael Gredenhoff and has been published by Stockholm School of Economics Efi Economic Research Institut this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Business & Economics categories.

A Primer On Bootstrap Testing Of Hypotheses In Time Series Models

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Author : Giuseppe Cavaliere
language : en
Publisher:
Release Date : 2019

A Primer On Bootstrap Testing Of Hypotheses In Time Series Models written by Giuseppe Cavaliere and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Model Free Prediction And Regression

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Author : Dimitris N. Politis
language : en
Publisher: Springer
Release Date : 2015-11-13

Model Free Prediction And Regression written by Dimitris N. Politis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-13 with Mathematics categories.

The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.

Identification And Inference For Econometric Models

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Author : Donald W. K. Andrews
language : en
Publisher: Cambridge University Press
Release Date : 2005-07-04

Identification And Inference For Econometric Models written by Donald W. K. Andrews 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 2005-07-04 with Business & Economics categories.

This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Time Series

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Author : Tucker S McElroy
language : en
Publisher: CRC Press
Release Date : 2021-06-30

Time Series written by Tucker S McElroy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-30 with categories.

Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness, (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.S. students. Basic theoretical results are presented in a mathematically convincing way, and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results. The book provides the foundation of time series methods, including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth, as well as frequency domain methods. Entropy and other information theoretic notions are introduced, with applications to time series modeling. The second half of the book focuses on statistical inference, the fitting of time series models, as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail, but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain, the discussion of entropy maximization, and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises, half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples, as well as the solutions to exercises.

Generalized Method Of Moments

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Author : Alastair R. Hall
language : en
Publisher: OUP Oxford
Release Date : 2004-12-23

Generalized Method Of Moments written by Alastair R. Hall and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-23 with Business & Economics categories.

Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empirical examples in macroeconomics and finance. Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test and tests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrument asymptotics.

Essays On Bootstrap In Econometrics

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Author : Maximilien Kaffo Melou
language : en
Publisher:
Release Date : 2014

Essays On Bootstrap In Econometrics written by Maximilien Kaffo Melou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

Reproducible Econometrics Using R

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Author : Jeffrey S. Racine
language : en
Publisher: Oxford University Press, USA
Release Date : 2019-01-23

Reproducible Econometrics Using R written by Jeffrey S. Racine and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-23 with Business & Economics categories.

Linear time series methods -- Introduction to linear time series models -- Random walks, unit roots, and spurious relationships -- Univariate linear time series models -- Robust parametric inference -- Robust parametric estimation -- Model uncertainty -- Advance -- Bibliography -- Author index -- Subject index

Time Series Econometrics

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Author : Pierre Perron
language : en
Publisher:
Release Date : 2018

Time Series Econometrics written by Pierre Perron and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Econometrics categories.

Part I. Unit roots and trend breaks -- Part II. Structural change

Handbook Of Computational Econometrics

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Author : David A. Belsley
language : en
Publisher: John Wiley & Sons
Release Date : 2009-08-18

Handbook Of Computational Econometrics written by David A. Belsley and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-18 with Mathematics categories.

Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization, and estimation. Each topic is fully introduced before proceeding to a more in-depth examination of the relevant methodologies and valuable illustrations. This book: Provides self-contained treatments of issues in computational econometrics with illustrations and invaluable bibliographies. Brings together contributions from leading researchers. Develops the techniques needed to carry out computational econometrics. Features network studies, non-parametric estimation, optimization techniques, Bayesian estimation and inference, testing methods, time-series analysis, linear and nonlinear methods, VAR analysis, bootstrapping developments, signal extraction, software history and evaluation. This book will appeal to econometricians, financial statisticians, econometric researchers and students of econometrics at both graduate and advanced undergraduate levels.