Consistent Variance of the Laplace-Type Estimators PDF Download
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Author: Anna Kormilitsina
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The Laplace-type estimator has become popular in applied macroeconomics, in particular for estimation of dynamic stochastic general equilibrium (DSGE) models. It is often obtained as the mean and variance of a parameter's quasi-posterior distribution, which is defined using a classical estimation objective. We demonstrate that the objective must be properly scaled; otherwise, arbitrarily small confidence intervals can be obtained if calculated directly from the quasi-posterior distribution. We estimate a standard DSGE model and find that scaling up the objective may be useful in estimation with problematic parameter identification. It this case, however, it is important to adjust the quasi-posterior variance to obtain valid confidence intervals.
Author: Anna Kormilitsina
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The Laplace-type estimator has become popular in applied macroeconomics, in particular for estimation of dynamic stochastic general equilibrium (DSGE) models. It is often obtained as the mean and variance of a parameter's quasi-posterior distribution, which is defined using a classical estimation objective. We demonstrate that the objective must be properly scaled; otherwise, arbitrarily small confidence intervals can be obtained if calculated directly from the quasi-posterior distribution. We estimate a standard DSGE model and find that scaling up the objective may be useful in estimation with problematic parameter identification. It this case, however, it is important to adjust the quasi-posterior variance to obtain valid confidence intervals.
Author: John B. Taylor
Publisher: Elsevier
ISBN: 0444594787
Category : Business & Economics
Languages : en
Pages : 1376
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Book Description
Handbook of Macroeconomics surveys all major advances in macroeconomic scholarship since the publication of Volume 1 (1999), carefully distinguishing between empirical, theoretical, methodological, and policy issues. It courageously examines why existing models failed during the financial crisis, and also addresses well-deserved criticism head on. With contributions from the world's chief macroeconomists, its reevaluation of macroeconomic scholarship and speculation on its future constitute an investment worth making. Serves a double role as a textbook for macroeconomics courses and as a gateway for students to the latest research Acts as a one-of-a-kind resource as no major collections of macroeconomic essays have been published in the last decade
Author: Han Hong
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Odile Pons
Publisher: World Scientific
ISBN: 9811210705
Category : Mathematics
Languages : en
Pages : 304
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Book Description
The approximation and the estimation of nonparametric functions by projections on an orthonormal basis of functions are useful in data analysis. This book presents series estimators defined by projections on bases of functions, they extend the estimators of densities to mixture models, deconvolution and inverse problems, to semi-parametric and nonparametric models for regressions, hazard functions and diffusions. They are estimated in the Hilbert spaces with respect to the distribution function of the regressors and their optimal rates of convergence are proved. Their mean square errors depend on the size of the basis which is consistently estimated by cross-validation. Wavelets estimators are defined and studied in the same models.The choice of the basis, with suitable parametrizations, and their estimation improve the existing methods and leads to applications to a wide class of models. The rates of convergence of the series estimators are the best among all nonparametric estimators with a great improvement in multidimensional models. Original methods are developed for the estimation in deconvolution and inverse problems. The asymptotic properties of test statistics based on the estimators are also established.
Author: Balvant Keshav Kale
Publisher: Alpha Science Int'l Ltd.
ISBN: 9781842652190
Category : Business & Economics
Languages : en
Pages : 312
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Book Description
"After a brief historical perspective, A First Course on Parametric Inference, discusses the basic concept of sufficient statistic and the classical approach based on minimum variance unbiased estimator. There is a separate chapter on simultaneous estimation of several parameters. Large sample theory of estimation, based on consistent asymptotically normal estimators obtained by method of moments, percentile and the method of maximum likelihood is also introduced. The tests of hypotheses for finite samples with classical Neyman-Pearson theory is developed pointing out its connection with Bayesian approach. The hypotheses testing and confidence interval techniques are developed leading to likelihood ratio tests, score tests and tests based on maximum likelihood estimators."--BOOK JACKET.
Author: Shayle R. Searle
Publisher: John Wiley & Sons
ISBN: 0470317698
Category : Mathematics
Languages : en
Pages : 537
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Book Description
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. ". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models." —Journal of the American Statistical Association "This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation." —Mathematical Reviews "The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation." —Technometrics This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.
Author: Peter W. Glynn
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
The principal problem associated with steady-state simulation is the estimation of the variance term in an associated central limit theorem. This paper develops several strongly consistent estimates for this term using the strong approximations available for Brownian motion. A comparison of rates of convergence is given for a variety of estimators. Keywords: confidence intervals; regenerative simulation; simulation output analysis; strongly consistent estimation.
Author: Marie Davidian
Publisher: Springer
ISBN: 3319058010
Category : Mathematics
Languages : en
Pages : 599
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Book Description
This volume contains Raymond J. Carroll's research and commentary on its impact by leading statisticians. Each of the seven main parts focuses on a key research area: Measurement Error, Transformation and Weighting, Epidemiology, Nonparametric and Semiparametric Regression for Independent Data, Nonparametric and Semiparametric Regression for Dependent Data, Robustness, and other work. The seven subject areas reviewed in this book were chosen by Ray himself, as were the articles representing each area. The commentaries not only review Ray’s work, but are also filled with history and anecdotes. Raymond J. Carroll’s impact on statistics and numerous other fields of science is far-reaching. His vast catalog of work spans from fundamental contributions to statistical theory to innovative methodological development and new insights in disciplinary science. From the outset of his career, rather than taking the “safe” route of pursuing incremental advances, Ray has focused on tackling the most important challenges. In doing so, it is fair to say that he has defined a host of statistics areas, including weighting and transformation in regression, measurement error modeling, quantitative methods for nutritional epidemiology and non- and semiparametric regression.
Author: Yacine Aït-Sahalia
Publisher: Princeton University Press
ISBN: 0691161437
Category : Business & Economics
Languages : en
Pages : 683
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Book Description
A comprehensive introduction to the statistical and econometric methods for analyzing high-frequency financial data High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.
Author: Eugene Demidenko
Publisher: John Wiley & Sons
ISBN: 1118091574
Category : Mathematics
Languages : en
Pages : 768
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Book Description
Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.
