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| estimation inference and specification analysis: Estimation, Inference and Specification Analysis Halbert White, 1996-06-28 This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated. |
| estimation inference and specification analysis: The SAGE Handbook of Regression Analysis and Causal Inference Henning Best, Christof Wolf, 2013-12-20 ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis. |
| estimation inference and specification analysis: Dynamic Nonlinear Econometric Models Benedikt M. Pötscher, Ingmar R. Prucha, 2013-03-09 Many relationships in economics, and also in other fields, are both dynamic and nonlinear. A major advance in econometrics over the last fifteen years has been the development of a theory of estimation and inference for dy namic nonlinear models. This advance was accompanied by improvements in computer technology that facilitate the practical implementation of such estimation methods. In two articles in Econometric Reviews, i.e., Pötscher and Prucha {1991a,b), we provided -an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature up to the beginning of this decade. Among others, the class of M-estimators contains least mean distance estimators (includ ing maximum likelihood estimators) and generalized method of moment estimators. The present book expands and revises the discussion in those articles. It is geared towards the professional econometrician or statistician. Besides reviewing the literature we also presented in the above men tioned articles a number of then new results. One example is a consis tency result for the case where the identifiable uniqueness condition fails. |
| estimation inference and specification analysis: Handbook of Applied Economic Statistics Aman Ullah, 1998-02-03 This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series. |
| estimation inference and specification analysis: Essays in Honor of Jerry Hausman Badi H. Baltagi, Whitney Newey, Hal White, R. Carter Hill, 2012-12-17 Aims to annually publish original scholarly econometrics papers on designated topics with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics throughout the empirical economic, business and social science literature. |
| estimation inference and specification analysis: Academic Press Library in Signal Processing, Volume 7 , 2017-12-01 Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering is aimed at university researchers, post graduate students and R&D engineers in the industry, providing a tutorial-based, comprehensive review of key topics and technologies of research in Array and Radar Processing, Communications Engineering and Machine Learning. Users will find the book to be an invaluable starting point to their research and initiatives. With this reference, readers will quickly grasp an unfamiliar area of research, understand the underlying principles of a topic, learn how a topic relates to other areas, and learn of research issues yet to be resolved. - Presents a quick tutorial of reviews of important and emerging topics of research - Explores core principles, technologies, algorithms and applications - Edited and contributed by international leading figures in the field - Includes comprehensive references to journal articles and other literature upon which to build further, more detailed knowledge |
| estimation inference and specification analysis: Topics in Modelling of Clustered Data Marc Aerts, Geert Molenberghs, Louise M. Ryan, Helena Geys, 2002-05-29 Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and s |
| estimation inference and specification analysis: Diagnostic Checks in Time Series Wai Keung Li, 2003-12-29 Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks. Diagnostic Checks in Time Series helps to fill that |
| estimation inference and specification analysis: Valuing Environmental Preferences Ian J. Bateman, Kenneth G. Willis, 2001-11-01 Just as individuals have preferences regarding the various goods and services they purchase every day, so they also hold preferences regrding public goods such as hose provided by the naural environment. However, unlike provate goods, environmental goods often cannot be valued by direct reference o any market price. Thsi amkes economic analysis of the costs and benefits of environmental change problematic. Over the past few decades a number of methods have developed to address this problem by attempting to value environmental preferences. Principal among hese has been the contingent valuation (CV) method which uses surveys to ask individuals how much they would be willing to pay or willing to accept in compensation for gains and losses of environmental goods. The period from the mid-1980s to the present day has seen a m,assive expansion in use of the CV method. From its originalroots int eh USA, through Europe and the developed world, the method has now reached worldwide application with a substantial proportion of current studies being undertaken in developing countries where environmental services are often the dominating determinant of everyday living standards. The method has simultaneously moved from the realm of pure academic speculation into the sphere of instiutional decision analysis. However, the past decade also witness a developing critique of the CV method with a number of commentators questioning the underlying validity of its dervied valuations. This volume, therefore, reflects a time of heated debate, as wellas from commentators who see it as an interesting experimental tool regardless of the question of absolute validity of estimates. The book embraces the theoritical, methodologicl, empirical, and institutional aspects of the current debate. It covers US, European , and developing country applications, and the institutional frameworks within which CV studies are applied. |
| estimation inference and specification analysis: Econometric Foundations Pack with CD-ROM Ron Mittelhammer (Prof.), George G. Judge, Douglas J. Miller, 2000-07-28 The text and accompanying CD-ROM develop step by step a modern approach to econometric problems. They are aimed at talented upper-level undergraduates, graduate students, and professionals wishing to acquaint themselves with the pinciples and procedures for information processing and recovery from samples of economic data. The text fully provides an operational understanding of a rich set of estimation and inference tools, including tradional likelihood based and non-traditional non-likelihood based procedures, that can be used in conjuction with the computer to address economic problems. |
| estimation inference and specification analysis: Applied Logistic Regression David W. Hosmer, Jr., Stanley Lemeshow, Rodney X. Sturdivant, 2013-02-26 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines. |
| estimation inference and specification analysis: Handbook of Economic Forecasting G. Elliott, C. W.J. Granger, A. G. Timmermann, 2006-07-14 Section headings in this handbook include: 'Forecasting Methodology; 'Forecasting Models'; 'Forecasting with Different Data Structures'; and 'Applications of Forecasting Methods.'. |
| estimation inference and specification analysis: Oxford Textbook of Global Public Health Roger Detels, Quarraisha Abdool Karim, Fran Baum, Liming Li, Alastair H. Leyland, 2022 Invaluable for all practitioners, trainees, and students of public health and epidemiology, the Oxford Textbook of Global Public Health covers the scope, methods, and practice of public health and has been comprehensively updated for its seventh edition. |
| estimation inference and specification analysis: Who Loses in the Downturn? Herwig Immervoll, Andreas Peichl, Konstantinos Tatsiramos, 2011-04-15 Contains fresh knowledge on the effects of the economic downturn on employment and income distribution. This title also contains research papers offering fresh insights into issues such as how wages, employment and incomes are affected by the crisis, which demographic groups are most vulnerable in the recession, and more. |
| estimation inference and specification analysis: Statistical Foundations of Econometric Modelling Aris Spanos, 1986-10-30 A thorough foundation in probability theory and statistical inference provides an introduction to the underlying theory of econometrics that motivates the student at a intuitive as well as a formal level. |
| estimation inference and specification analysis: Statistical Machine Learning Richard Golden, 2020-06-24 The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. |
| estimation inference and specification analysis: Mathematical Perspectives on Neural Networks Paul Smolensky, Michael C. Mozer, 2013-05-13 Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field. |
| estimation inference and specification analysis: Nonparametric and Semiparametric Methods in Econometrics and Statistics William A. Barnett, James Powell, George E. Tauchen, 1991-06-28 Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data. |
| estimation inference and specification analysis: Time Series Models D.R. Cox, D.V. Hinkley, O.E. Barndorff-Nielsen, 2020-11-26 The analysis prediction and interpolation of economic and other time series has a long history and many applications. Major new developments are taking place, driven partly by the need to analyze financial data. The five papers in this book describe those new developments from various viewpoints and are intended to be an introduction accessible to readers from a range of backgrounds. The book arises out of the second Seminaire European de Statistique (SEMSTAT) held in Oxford in December 1994. This brought together young statisticians from across Europe, and a series of introductory lectures were given on topics at the forefront of current research activity. The lectures form the basis for the five papers contained in the book. The papers by Shephard and Johansen deal respectively with time series models for volatility, i.e. variance heterogeneity, and with cointegration. Clements and Hendry analyze the nature of prediction errors. A complementary review paper by Laird gives a biometrical view of the analysis of short time series. Finally Astrup and Nielsen give a mathematical introduction to the study of option pricing. Whilst the book draws its primary motivation from financial series and from multivariate econometric modelling, the applications are potentially much broader. |
| estimation inference and specification analysis: Asymptotic Theory for Econometricians Halbert White, 2014-06-28 This book is intended to provide a somewhat more comprehensive and unified treatment of large sample theory than has been available previously and to relate the fundamental tools of asymptotic theory directly to many of the estimators of interest to econometricians. In addition, because economic data are generated in a variety of different contexts (time series, cross sections, time series--cross sections), we pay particular attention to the similarities and differences in the techniques appropriate to each of these contexts. |
| estimation inference and specification analysis: Topics in Structural VAR Econometrics Carlo Giannini, 2013-11-11 1. Introduction 1 2. Identification Analysis and F.I.M.L. Estimation for the K-Mode1 10 3. Identification Analysis and F.I.ML. Estimation for the C-Model 23 4. Identification Analysis and F.I.M.L. Estimation for the AB-Model 32 5. Impulse Response Analysis and Forecast Error Variance Decomposition in SVAR Modeling 44 5 .a Impulse Response Analysis 44 5.b Variance Decomposition (by Antonio Lanzarotti) 51 6. Long-run A-priori Information. Deterministic Components. Cointegration 58 6.a Long-run A-priori Information 58 6.b Deterministic Components 62 6.c Cointegration 65 7. The Working of an AB-Model 71 Annex 1: The Notions ofReduced Form and Structure in Structural VAR Modeling 83 Annex 2: Some Considerations on the Semantics, Choice and Management of the K, C and AB-Models 87 Appendix A 93 Appendix B 96 Appendix C (by Antonio Lanzarotti and Mario Seghelini) 99 Appendix D (by Antonio Lanzarotti and Mario Seghelini) 109 References 128 Foreword In recent years a growing interest in the structural VAR approach (SVAR) has followed the path-breaking works by Blanchard and Watson (1986), Bemanke (1986) and Sims (1986), especially in U.S. applied macroeconometric literature. The approach can be used in two different, partially overlapping directions: the interpretation ofbusiness cycle fluctuations of a small number of significantmacroeconomic variables and the identification of the effects of different policies. |
| estimation inference and specification analysis: Topics in Structural VAR Econometrics Gianni Amisano, Carlo Giannini, 2012-12-06 In recent years a growing interest in the structural V AR approach (SV AR) has followed the path-breaking works by Blanchard and Watson (1986), Bernanke (1986) and Sims (1986), especially in the U.S. applied macroeconometric literature. The approach can be used in two different, partially overlapping, directions: the interpretation of business cycle fluctuations of a small number of significant macroeconomic variables and the identification of the effects of different policies. SV AR literature shows a common feature: the attempt to organise, in a structural theoretical sense, instantaneous correlations among the relevant variables. In non-structural V AR modelling, instead, correlations are normally hidden in the variance covariance matrix of the V AR model innovations. of independent V AR analysis tries to isolate (identify) a set shocks by means of a number of meaningful theoretical restrictions. The shocks can be regarded as the ultimate source of stochastic variation of the vector of variables which can all be seen as potentially endogenous. Looking at the development of SV AR literature we felt that it still lacked a formal general framework which could embrace the several types of models so far proposed for identification and estimation. This is the second edition of the book, which originally appeared as number 381 of the Springer series Lecture notes in Economics of the first edition was Carlo and Mathematical Systems. The author Giannini. |
| estimation inference and specification analysis: From Statistics to Neural Networks Vladimir Cherkassky, Jerome H. Friedman, Harry Wechsler, 2012-12-06 The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether over 100 participants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems. |
| estimation inference and specification analysis: Handbook of Epidemiology Wolfgang Ahrens, Iris Pigeot, 2007-07-26 The Handbook of Epidemiology provides a comprehensive overview of the field and thus bridges the gap between standard textbooks of epidemiology and dispersed publications for specialists that have a narrowed focus on specific areas. It reviews the key issues and methodological approaches pertinent to the field for which the reader pursues an expatiated overview. It thus serves both as a first orientation for the interested reader and as a starting point for an in-depth study of a specific area, as well as a quick reference and recapitulatory overview for the expert. The book includes topics that are usually missing in standard textbooks. |
| estimation inference and specification analysis: Handbook of Economic Forecasting Graham Elliott, Allan Timmermann, 2013-10-24 The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics |
| estimation inference and specification analysis: Econometric Modelling of European Money Demand Engelbert Plassmann, 2012-12-06 The introduction of a single European currency constitutes a remarkable instance of internationalization of monetary policy. Whether a concomitant internationalization can be detected also in the econometric foundations of monetary policy is the topic dealt with in this book. The basic theoretical ingredients comprise a data-driven approach to econometric modelling and a generalized approach to cross-sectional aggregation. The empirical result is a data-consistent structural money demand function isolated within a properly identified, dynamic macroeconomic system for Europe. The book itself evolved from a research project within the former Son derforschungsbereich SFB 178 Internationalization of the Economy at the University of Konstanz. Its finalization entails a due amount of gratitude to be extended into several directions: I am personally indebted, first of all, to my academic supervisor, Professor Dr. Nikolaus Laufer, for originally inspiring this work and for meticulously perusing its eventual result. Professor Dr. Win fried Pohlmeier, as a second supervisor, provided valuable confidence bounds around an earlier draft. The comments of both supervisors contributed substantially to the present shape of the book. I am institutionally indebted to the University of Konstanz, notably its Faculty of Economics and Statistics, for continuous provision of an excellent research environment, and to the Deutsche Forschungsgemeinschaft in Bonn for generous sponsorship of the former SFB, whose financial support dur ing that period is gratefully acknowledged. I am also indebted to Dresdner Bank AG Frankfurt, Risk Methodology Trading, for benign tolerance of all distractions associated with the preparation of the final manuscript. |
| estimation inference and specification analysis: Social Exclusion Giuliana Parodi, Dario Sciulli, 2011-09-24 The book provides a panoramic approach to social exclusion, with emphasis on structural causes (education, health, accidents) and on short term causes connected with the crisis which started in 2008. The picture emerging, based on econometric analysis, is that the crisis has widened the risk of social exclusion, from the structural groups, like disabled people and formerly convicted people, to other groups, like the young, unemployed, low skilled workers and immigrants, in terms of income, poverty, health, unemployment, transition between occupational statuses, participation, leading to a widening of socio-economic duality. It has also been stressed the relevance of definitions of socio-economic outcomes for the evaluation of the crisis, and their consequences to define interventions to fight socio-economic effects of the economic downturn. The adequacy of welfare policies to cope with social exclusion, especially during a crisis, has been called into question. |
| estimation inference and specification analysis: Forecasting Volatility in the Financial Markets Stephen Satchell, John Knight, 2002-08-22 'Forecasting Volatility in the Financial Markets' assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modelling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.The editors have brought together a set of contributors that give the reader a firm grounding in relevant theory and research and an insight into the cutting edge techniques applied in this field of the financial markets.This book is of particular relevance to anyone who wants to understand dynamic areas of the financial markets.* Traders will profit by learning to arbitrage opportunities and modify their strategies to account for volatility.* Investment managers will be able to enhance their asset allocation strategies with an improved understanding of likely risks and returns.* Risk managers will understand how to improve their measurement systems and forecasts, enhancing their risk management models and controls.* Derivative specialists will gain an in-depth understanding of volatility that they can use to improve their pricing models.* Students and academics will find the collection of papers an invaluable overview of this field.This book is of particular relevance to those wanting to understand the dynamic areas of volatility modeling and forecasting of the financial marketsProvides the latest research and techniques for Traders, Investment Managers, Risk Managers and Derivative Specialists wishing to manage their downside risk exposure Current research on the key forecasting methods to use in risk management, including two new chapters |
| estimation inference and specification analysis: Essays in Honour of Fabio Canova Juan J. Dolado, Luca Gambetti, Christian Matthes, 2022-09-21 Both parts of Volume 44 of Advances in Econometrics pay tribute to Fabio Canova for his major contributions to economics over the last four decades. |
| estimation inference and specification analysis: Modern Epidemiology Kenneth J. Rothman, Sander Greenland, Timothy L. Lash, 2008 The thoroughly revised and updated Third Edition of the acclaimed Modern Epidemiology reflects both the conceptual development of this evolving science and the increasingly focal role that epidemiology plays in dealing with public health and medical problems. Coauthored by three leading epidemiologists, with sixteen additional contributors, this Third Edition is the most comprehensive and cohesive text on the principles and methods of epidemiologic research. The book covers a broad range of concepts and methods, such as basic measures of disease frequency and associations, study design, field methods, threats to validity, and assessing precision. It also covers advanced topics in data analysis such as Bayesian analysis, bias analysis, and hierarchical regression. Chapters examine specific areas of research such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, and clinical epidemiology. |
| estimation inference and specification analysis: Essays in Econometrics Clive W. J. Granger, 2001-07-23 These are econometrician Clive W. J. Granger's major essays in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. |
| estimation inference and specification analysis: Piero Sraffa John Cunningham Wood, 1995 These volumes bring together 115 articles on this great economist. The work presents a detailed overview of the analytical writings from contemporary sources through to the present day |
| estimation inference and specification analysis: Generalized, Linear, and Mixed Models Charles E. McCulloch, Shayle R. Searle, John M. Neuhaus, 2011-09-20 An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects. A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the Second Edition features: A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation A new chapter that treats shared random effects models, latent class models, and properties of models A revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions Expanded coverage of marginal versus conditional models Numerous new and updated examples With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers. |
| estimation inference and specification analysis: Short-Memory Linear Processes and Econometric Applications Kairat T. Mynbaev, 2011-05-23 This book serves as a comprehensive source of asymptotic results for econometric models with deterministic exogenous regressors. Such regressors include linear (more generally, piece-wise polynomial) trends, seasonally oscillating functions, and slowly varying functions including logarithmic trends, as well as some specifications of spatial matrices in the theory of spatial models. The book begins with central limit theorems (CLTs) for weighted sums of short memory linear processes. This part contains the analysis of certain operators in Lp spaces and their employment in the derivation of CLTs. The applications of CLTs are to the asymptotic distribution of various estimators for several econometric models. Among the models discussed are static linear models with slowly varying regressors, spatial models, time series autoregressions, and two nonlinear models (binary logit model and nonlinear model whose linearization contains slowly varying regressors). The estimation procedures include ordinary and nonlinear least squares, maximum likelihood, and method of moments. Additional topical coverage includes an introduction to operators, probabilities, and linear models; Lp-approximable sequences of vectors; convergence of linear and quadratic forms; regressions with slowly varying regressors; spatial models; convergence; nonlinear models; and tools for vector autoregressions. |
| estimation inference and specification analysis: A Companion to Theoretical Econometrics Badi H. Baltagi, 2008-04-15 A Companion to Theoretical Econometrics provides a comprehensive reference to the basics of econometrics. This companion focuses on the foundations of the field and at the same time integrates popular topics often encountered by practitioners. The chapters are written by international experts and provide up-to-date research in areas not usually covered by standard econometric texts. Focuses on the foundations of econometrics. Integrates real-world topics encountered by professionals and practitioners. Draws on up-to-date research in areas not covered by standard econometrics texts. Organized to provide clear, accessible information and point to further readings. |
| estimation inference and specification analysis: Modelling Economic Series Clive William John Granger, 1990 This is a volume of readings for graduate students, especially those taking courses in applied econometrics, who need to learn how to evaluate the validity of present theories and techniques. The aim of the text is to aid readers in the difficult task of actually constructing models. The essays vary in the degree of technical sophistication used, but each paper intends to provide students with a sound knowledge of the practical difficulties of model specification, evaluation and interpretation, as well as advice on tackling these difficulties. |
| estimation inference and specification analysis: Transgender Rights and Politics Jami Kathleen Taylor, Donald P. Haider-Markel, 2014-10-14 A theoretically grounded and methodically sophisticated empirical analysis of transgender politics |
| estimation inference and specification analysis: Handbook of Empirical Economics and Finance Aman Ullah, David E. A. Giles, 2016-04-19 Handbook of Empirical Economics and Finance explores the latest developments in the analysis and modeling of economic and financial data. Well-recognized econometric experts discuss the rapidly growing research in economics and finance and offer insight on the future direction of these fields. Focusing on micro models, the first group of chapters describes the statistical issues involved in the analysis of econometric models with cross-sectional data often arising in microeconomics. The book then illustrates time series models that are extensively used in empirical macroeconomics and finance. The last set of chapters explores the types of panel data and spatial models that are becoming increasingly significant in analyzing complex economic behavior and policy evaluations. This handbook brings together both background material and new methodological and applied results that are extremely important to the current and future frontiers in empirical economics and finance. It emphasizes inferential issues that transpire in the analysis of cross-sectional, time series, and panel data-based empirical models in economics, finance, and related disciplines. |
| estimation inference and specification analysis: An Information Theoretic Approach to Econometrics George G. Judge, Ron C. Mittelhammer, 2011-12-12 This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family. |
| estimation inference and specification analysis: Grants and Awards for the Fiscal Year Ended ... National Science Foundation (U.S.), 1981 |
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Estimation in Statistics - GeeksforGeeks
May 8, 2024 · Estimation in statistics involves using sample data to make educated guesses about a population's characteristics, such as mean, variance, or proportion. The population …
What Is Estimation In Maths? Definition, Examples, Facts
Definition for estimate in math is an approximate value close enough to the correct value. A lot of guesses are made to make math easier and clearer. Estimation is a rough calculation of the …
ESTIMATION | English meaning - Cambridge Dictionary
ESTIMATION definition: 1. your opinion of someone or something: 2. a guess or calculation about the cost, size, value…. Learn more.
What is an estimation method? 6 techniques for project planning
Project estimation methods take constraints such as cost, scope, and time into account to accurately budget funds and resources needed for project success. In this guide, we’ll cover …
What is estimating? - BBC Bitesize
Estimating means making numbers simpler but keeping their value close to what it was. The result is not exact, but it gives an idea of what the total will be. There are steps which can be...
Estimation in Statistics
Describes the estimation process in statistics. Covers point estimates, interval estimates, confidence intervals, confidence levels, and margin of error.
Estimation - Wikipedia
Estimation (or estimating) is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. …
Estimation (Introduction) - Math is Fun
After doing some practice, read our page on Estimation Tips and Tricks. Estimating Counts, Lengths and More. Estimation is not always about doing calculations. It is important for you to …
ESTIMATION Definition & Meaning - Merriam-Webster
The meaning of ESTIMATION is judgment, opinion. How to use estimation in a sentence.
Estimation | Definition, Examples, & Facts | Britannica
estimation, in statistics, any of numerous procedures used to calculate the value of some property of a population from observations of a sample drawn from the population. A point estimate, for …
Estimation in Statistics - GeeksforGeeks
May 8, 2024 · Estimation in statistics involves using sample data to make educated guesses about a population's characteristics, such as mean, variance, or proportion. The population …
What Is Estimation In Maths? Definition, Examples, Facts
Definition for estimate in math is an approximate value close enough to the correct value. A lot of guesses are made to make math easier and clearer. Estimation is a rough calculation of the …
ESTIMATION | English meaning - Cambridge Dictionary
ESTIMATION definition: 1. your opinion of someone or something: 2. a guess or calculation about the cost, size, value…. Learn more.
What is an estimation method? 6 techniques for project planning
Project estimation methods take constraints such as cost, scope, and time into account to accurately budget funds and resources needed for project success. In this guide, we’ll cover …
What is estimating? - BBC Bitesize
Estimating means making numbers simpler but keeping their value close to what it was. The result is not exact, but it gives an idea of what the total will be. There are steps which can be...
Estimation in Statistics
Describes the estimation process in statistics. Covers point estimates, interval estimates, confidence intervals, confidence levels, and margin of error.
