Author : Yoosoon Chang
Publisher : Emerald Group Publishing
Page : 406 pages
File Size : 35,28 MB
Release : 2023-04-24
Category : Business & Economics
ISBN : 1837532087
Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.
Author : Matias D. Cattaneo
Publisher : Cambridge University Press
Page : 118 pages
File Size : 27,58 MB
Release : 2020-02-13
Category : Political Science
ISBN : 1108670423
In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.
Author : Christian Kleiber
Publisher : Springer Science & Business Media
Page : 229 pages
File Size : 19,61 MB
Release : 2008-12-10
Category : Business & Economics
ISBN : 0387773185
R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
Author : Juan Carlos Escanciano
Publisher : Emerald Group Publishing
Page : 539 pages
File Size : 15,71 MB
Release : 2017-05-11
Category : Business & Economics
ISBN : 1787143902
Volume 38 of Advances in Econometrics collects twelve innovative and thought-provoking contributions to the literature on Regression Discontinuity designs, covering a wide range of methodological and practical topics such as identification, interpretation, implementation, falsification testing, estimation and inference.
Author : Martin Huber
Publisher : MIT Press
Page : 337 pages
File Size : 15,69 MB
Release : 2023-08-01
Category : Business & Economics
ISBN : 0262545918
A comprehensive and cutting-edge introduction to quantitative methods of causal analysis, including new trends in machine learning. Reasoning about cause and effect—the consequence of doing one thing versus another—is an integral part of our lives as human beings. In an increasingly digital and data-driven economy, the importance of sophisticated causal analysis only deepens. Presenting the most important quantitative methods for evaluating causal effects, this textbook provides graduate students and researchers with a clear and comprehensive introduction to the causal analysis of empirical data. Martin Huber’s accessible approach highlights the intuition and motivation behind various methods while also providing formal discussions of key concepts using statistical notation. Causal Analysis covers several methodological developments not covered in other texts, including new trends in machine learning, the evaluation of interaction or interference effects, and recent research designs such as bunching or kink designs. Most complete and cutting-edge introduction to causal analysis, including causal machine learning Clean presentation of rigorous material avoids extraneous detail and emphasizes conceptual analogies over statistical notation Supplies a range of applications and practical examples using R
Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 12,49 MB
Release : 2009-11-11
Category : Mathematics
ISBN : 9781420074086
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Author : Vladik Kreinovich
Publisher : Springer
Page : 693 pages
File Size : 33,21 MB
Release : 2017-02-11
Category : Technology & Engineering
ISBN : 3319507427
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.
Author : Howell Tong
Publisher : Oxford University Press, USA
Page : 592 pages
File Size : 37,76 MB
Release : 1990
Category : Mathematics
ISBN :
Written by an internationally recognized expert in the field, this book provides a valuable introduction to the rapidly growing area of non-linear time series. Because developments in the study of dynamical systems have motivated many of the advances discussed here, the author's coverage includes such fundamental concepts of dynamical systems theory as limit cycles, Lyapunov functions, thresholds, and stability, with detailed descriptions of their role in the analysis of non-linear time series data. As the first accessible and comprehensive account of these exciting new developments, this unique volume bridges the gap between linear and chaotic time series analysis. Both statisticians and dynamical systems theorists will value its survey of recent developments and the present state of research, as well as the discussion of a number of unsolved problems in the field.
Author : Andrew Gelman
Publisher : Cambridge University Press
Page : 551 pages
File Size : 50,54 MB
Release : 2021
Category : Business & Economics
ISBN : 110702398X
A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
Author : Lucien Le Cam
Publisher : Springer Science & Business Media
Page : 767 pages
File Size : 12,97 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1461249465
This book grew out of lectures delivered at the University of California, Berkeley, over many years. The subject is a part of asymptotics in statistics, organized around a few central ideas. The presentation proceeds from the general to the particular since this seemed the best way to emphasize the basic concepts. The reader is expected to have been exposed to statistical thinking and methodology, as expounded for instance in the book by H. Cramer [1946] or the more recent text by P. Bickel and K. Doksum [1977]. Another pos sibility, closer to the present in spirit, is Ferguson [1967]. Otherwise the reader is expected to possess some mathematical maturity, but not really a great deal of detailed mathematical knowledge. Very few mathematical objects are used; their assumed properties are simple; the results are almost always immediate consequences of the definitions. Some objects, such as vector lattices, may not have been included in the standard background of a student of statistics. For these we have provided a summary of relevant facts in the Appendix. The basic structures in the whole affair are systems that Blackwell called "experiments" and "transitions" between them. An "experiment" is a mathe matical abstraction intended to describe the basic features of an observational process if that process is contemplated in advance of its implementation. Typically, an experiment consists of a set E> of theories about what may happen in the observational process.
