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| an introduction to econometrics a self contained approach: An Introduction to Econometrics Frank Westhoff, 2013 Solutions to odd-numbered prep questions, review questions, and exercises in an undergraduate econometric textbook designed to teach students regression analysis on one semester. Solutions to odd-numbered prep questions, review questions, and exercises in an undergraduate econometric textbook designed to teach students regression analysis on one semester. |
| an introduction to econometrics a self contained approach: An Introduction to Econometrics , 2013 |
| an introduction to econometrics a self contained approach: Student Solutions Manual to Accompany 'An Introduction to Econometrics - a Self Contained Approach': Descriptive Statistics; Chapter 2 Essentials of Probability and Estimation; Chapter 3 Interval Estimates and the Central Limit Theorem; Chapter 4 Estimation Procedures, Estimates, and Hypothesis Testing; Chapter 5 Ordinary Least Squares Estimation Procedure-The Mechanics; Chapter 6 Ordinary Least Squares Estimation Procedure-The Properties; Chapter 7 Estimating the Variance of an Estimate's Property Distribution; Chapter 8 Interval Estimates and Hypothesis Testing; Chapter 9 One-Tailed Tests, Two-Tailed Tests, and Logarithms Frank Westhoff, 2013 Solutions to odd-numbered prep questions, review questions, and exercises in an undergraduate econometric textbook designed to teach students regression analysis on one semester. |
| an introduction to econometrics a self contained approach: Intermediate Statistics and Econometrics Dale J. Poirier, 1995 The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses-giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics. Poirier's text provides a thoroughly modern, self-contained, comprehensive, and accessible treatment of the probability and statistical foundations of econometrics with special emphasis on the linear regression model. Written primarily for advanced undergraduate and graduate students who are pursuing research careers in economics, Intermediate Statistics and Econometrics offers a broad perspective, bringing together a great deal of diverse material. Its comparative approach, emphasis on regression and prediction, and numerous exercises and references provide a solid foundation for subsequent courses in econometrics and will prove a valuable resource to many nonspecialists who want to update their quantitative skills. The introduction closes with an example of a real-world data set-the Challengerspace shuttle disaster-that motivates much of the text's theoretical discussion. The ten chapters that follow cover basic concepts, special distributions, distributions of functions of random variables, sampling theory, estimation, hypothesis testing, prediction, and the linear regression model. Appendixes contain a review of matrix algebra, computation, and statistical tables. |
| an introduction to econometrics a self contained approach: An Introduction to Mathematical Analysis for Economic Theory and Econometrics Dean Corbae, Maxwell Stinchcombe, Juraj Zeman, 2009-02-17 Providing an introduction to mathematical analysis as it applies to economic theory and econometrics, this book bridges the gap that has separated the teaching of basic mathematics for economics and the increasingly advanced mathematics demanded in economics research today. Dean Corbae, Maxwell B. Stinchcombe, and Juraj Zeman equip students with the knowledge of real and functional analysis and measure theory they need to read and do research in economic and econometric theory. Unlike other mathematics textbooks for economics, An Introduction to Mathematical Analysis for Economic Theory and Econometrics takes a unified approach to understanding basic and advanced spaces through the application of the Metric Completion Theorem. This is the concept by which, for example, the real numbers complete the rational numbers and measure spaces complete fields of measurable sets. Another of the book's unique features is its concentration on the mathematical foundations of econometrics. To illustrate difficult concepts, the authors use simple examples drawn from economic theory and econometrics. Accessible and rigorous, the book is self-contained, providing proofs of theorems and assuming only an undergraduate background in calculus and linear algebra. Begins with mathematical analysis and economic examples accessible to advanced undergraduates in order to build intuition for more complex analysis used by graduate students and researchers Takes a unified approach to understanding basic and advanced spaces of numbers through application of the Metric Completion Theorem Focuses on examples from econometrics to explain topics in measure theory |
| an introduction to econometrics a self contained approach: Econometric Modeling David F. Hendry, Bent Nielsen, 2012-06-21 Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research. |
| an introduction to econometrics a self contained approach: Contributions to Econometrics: Volume 1 John Denis Sargan, 1988-06-16 |
| an introduction to econometrics a self contained approach: Using R for Introductory Econometrics Florian Heiss, 2020-05-24 Introduces the popular, powerful and free programming language and software package R Focus implementation of standard tools and methods used in econometrics Compatible with Introductory Econometrics by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation Companion website with full text, all code for download and other goodies: http: //urfie.net Also check out Using Python for Introductory Econometrics http: //upfie.net/ Praise A very nice resource for those wanting to use R in their introductory econometrics courses. (Jeffrey M. Wooldridge) Using R for Introductory Econometrics is a fabulous modern resource. I know I'm going to be using it with my students, and I recommend it to anyone who wants to learn about econometrics and R at the same time. (David E. Giles in his blog Econometrics Beat) Topics: A gentle introduction to R Simple and multiple regression in matrix form and using black box routines Inference in small samples and asymptotics Monte Carlo simulations Heteroscedasticity Time series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited dependent variables: binary, count data, censoring, truncation, and sample selection Formatted reports and research papers combining R with R Markdown or LaTeX |
| an introduction to econometrics a self contained approach: Introduction to the Mathematical and Statistical Foundations of Econometrics Herman J. Bierens, 2004-12-20 This book is intended for use in a rigorous introductory PhD level course in econometrics. |
| an introduction to econometrics a self contained approach: Panel Data Econometrics Mike Tsionas, 2019-06-19 Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts |
| an introduction to econometrics a self contained approach: Microeconometrics A. Colin Cameron, Pravin K. Trivedi, 2005-05-09 This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets. |
| an introduction to econometrics a self contained approach: Econometric Analysis of Cross Section and Panel Data, second edition Jeffrey M. Wooldridge, 2010-10-01 The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of generalized instrumental variables (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the generalized estimating equation literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain obvious procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights. |
| an introduction to econometrics a self contained approach: Mathematical Methods and Models for Economists Angel de la Fuente, Ángel de la Fuente, 2000-01-28 A textbook for a first-year PhD course in mathematics for economists and a reference for graduate students in economics. |
| an introduction to econometrics a self contained approach: Applied Econometrics with R Christian Kleiber, Achim Zeileis, 2008-12-10 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. |
| an introduction to econometrics a self contained approach: Econometrics Fumio Hayashi, 2011-12-12 The most authoritative and comprehensive synthesis of modern econometrics available Econometrics provides first-year graduate students with a thoroughly modern introduction to the subject, covering all the standard material necessary for understanding the principal techniques of econometrics, from ordinary least squares through cointegration. The book is distinctive in developing both time-series and cross-section analysis fully, giving readers a unified framework for understanding and integrating results. Econometrics covers all the important topics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models, such as probit and tobit, are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient way. Virtually all the chapters include empirical applications drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises provide students with hands-on experience applying the techniques covered. The exposition is rigorous yet accessible, requiring a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text. For students who intend to write a thesis on applied topics, the empirical applications in Econometrics are an excellent way to learn how to conduct empirical research. For theoretically inclined students, the no-compromise treatment of basic techniques is an ideal preparation for more advanced theory courses. |
| an introduction to econometrics a self contained approach: Introductory Econometrics for Finance Chris Brooks, 2008-05-22 This best-selling textbook addresses the need for an introduction to econometrics specifically written for finance students. Key features: • Thoroughly revised and updated, including two new chapters on panel data and limited dependent variable models • Problem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and confidence to estimate and interpret models • Detailed examples and case studies from finance show students how techniques are applied in real research • Sample instructions and output from the popular computer package EViews enable students to implement models themselves and understand how to interpret results • Gives advice on planning and executing a project in empirical finance, preparing students for using econometrics in practice • Covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods • Thoroughly class-tested in leading finance schools. Bundle with EViews student version 6 available. Please contact us for more details. |
| an introduction to econometrics a self contained approach: Discrete Choice Methods with Simulation Kenneth Train, 2009-07-06 This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing. |
| an introduction to econometrics a self contained approach: Introduction to Econometrics, Student Value Edition James H. Stock, Mark W. Watson, 2018-11-06 Ensure students grasp the relevance of econometrics with Introduction to Econometrics -- the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics.-Publisher's description. |
| an introduction to econometrics a self contained approach: Introductory Econometrics Jeffrey M. Wooldridge, 2009 INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e International Edition illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed , leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects. |
| an introduction to econometrics a self contained approach: Econometrica , 1963 |
| an introduction to econometrics a self contained approach: Bayesian Econometric Modelling for Big Data Hang Qian, 2025-06-20 This book delves into scalable Bayesian statistical methods designed to tackle the challenges posed by big data. It explores a variety of divide-and-conquer and subsampling techniques, seamlessly integrating these scalable methods into a broad spectrum of econometric models. In addition to its focus on big data, the book introduces novel concepts within traditional statistics, such as the summation, subtraction, and multiplication of conjugate distributions. These arithmetic operators conceptualize pseudo data in the conjugate prior, sufficient statistics that determine the likelihood, and the posterior as a balance between data and prior information, adding an intriguing dimension to Bayesian analysis. This book also offers a deep dive into Bayesian computation. Given the intricacies of floating-point representation of real numbers, computer programs can sometimes yield unexpected or theoretically impossible results. Drawing from his experience as a senior statistical software developer, the author shares valuable strategies for designing numerically stable algorithms. The book is an essential resource for a diverse audience: graduate students seeking foundational knowledge in Bayesian econometric models, early-career statisticians eager to explore cutting-edge advancements in scalable Bayesian methods, data analysts struggling with out-of-memory challenges in large datasets, and statistical software users and developers striving to program with efficiency and numerical stability. |
| an introduction to econometrics a self contained approach: Methods for Estimation and Inference in Modern Econometrics Stanislav Anatolyev, Nikolay Gospodinov, 2011-06-07 This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference. |
| an introduction to econometrics a self contained approach: Econometric Modelling with Time Series Vance Martin, Stan Hurn, David Harris, 2013 Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and full-information maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function (pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasi-maximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter 10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulation-based estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the role of probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn-- publisher. |
| an introduction to econometrics a self contained approach: Handbook of Econometrics James Joseph Heckman, Edward E. Leamer, 2007 As conceived by the founders of the Econometric Society, econometrics is a field that uses economic theory and statistical methods to address empirical problems in economics. It is a tool for empirical discovery and policy analysis. The chapters in this volume embody this vision and either implement it directly or provide the tools for doing so. This vision is not shared by those who view econometrics as a branch of statistics rather than as a distinct field of knowledge that designs methods of inference from data based on models of human choice ... |
| an introduction to econometrics a self contained approach: Econometric Theory James Davidson, 2000-04-17 This book surveys recent developments in the rapidly expanding field of asymptotic distribution theory, placing special emphasis on the problems of time-dependence and heterogeneity. It is technically self-contained, with all but the most basic mathematical prerequisites being explained in their context. |
| an introduction to econometrics a self contained approach: Statistical Tools for Program Evaluation Jean-Michel Josselin, Benoît Le Maux, 2017-05-23 This book provides a self-contained presentation of the statistical tools required for evaluating public programs, as advocated by many governments, the World Bank, the European Union, and the Organization for Economic Cooperation and Development. After introducing the methodological framework of program evaluation, the first chapters are devoted to the collection, elementary description and multivariate analysis of data as well as the estimation of welfare changes. The book then successively presents the tools of ex-ante methods (financial analysis, budget planning, cost-benefit, cost-effectiveness and multi-criteria evaluation) and ex-post methods (benchmarking, experimental and quasi-experimental evaluation). The step-by-step approach and the systematic use of numerical illustrations equip readers to handle the statistics of program evaluation. It not only offers practitioners from public administrations, consultancy firms and nongovernmental organizations the basic tools and advanced techniques used in program assessment, it is also suitable for executive management training, upper undergraduate and graduate courses, as well as for self-study. |
| an introduction to econometrics a self contained approach: Mostly Harmless Econometrics Joshua D. Angrist, Jörn-Steffen Pischke, 2009-01-04 In addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. The authors explain why fancier econometric techniques are typically unnecessary and even dangerous. |
| an introduction to econometrics a self contained approach: JOURNAL OF ECONOMETRICS SUBJECT INDEX VOLUME 1-10 1973-1979 DENNIS AIGNER, 1979 |
| an introduction to econometrics a self contained approach: Introductory Econometrics: A Modern Approach Jeffrey M. Wooldridge, 2019-01-04 Gain an understanding of how econometrics can answer today's questions in business, policy evaluation and forecasting with Wooldridge's INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 7E. This edition's practical, yet professional, approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines. Information is organized around the type of data being analyzed, using a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, this edition incorporates more than 100 intriguing data sets in different formats. Updates introduce the latest developments in the field, including recent advances in the so-called “causal effects” or “treatment effects” literature, for an understanding of the impact and importance of econometrics today. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| an introduction to econometrics a self contained approach: Recursive Methods in Economic Dynamics Nancy L. Stokey, 1989-10-10 This rigorous but brilliantly lucid book presents a self-contained treatment of modern economic dynamics. Stokey, Lucas, and Prescott develop the basic methods of recursive analysis and illustrate the many areas where they can usefully be applied. |
| an introduction to econometrics a self contained approach: 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. |
| an introduction to econometrics a self contained approach: Handbook of Econometrics James J. Heckman, Edward Leamer, 2007-12-13 As conceived by the founders of the Econometric Society, econometrics is a field that uses economic theory and statistical methods to address empirical problems in economics. It is a tool for empirical discovery and policy analysis. The chapters in this volume embody this vision and either implement it directly or provide the tools for doing so. This vision is not shared by those who view econometrics as a branch of statistics rather than as a distinct field of knowledge that designs methods of inference from data based on models of human choice behavior and social interactions. All of the essays in this volume and its companion volume 6B offer guidance to the practitioner on how to apply the methods they discuss to interpret economic data. The authors of the chapters are all leading scholars in the fields they survey and extend.*Part of the renowned Handbooks in Economics Series*Updates and expands the exisiting Handbook of Econometrics volumes*An invaluable reference written by some of the world's leading econometricians. |
| an introduction to econometrics a self contained approach: Bayesian Statistics and Marketing Peter E. Rossi, Greg M. Allenby, Rob McCulloch, 2012-05-14 The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods. Written by the leading experts in the field, this unique book: Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models. Provides a self-contained introduction to Bayesian methods. Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems. Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies. Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike. |
| an introduction to econometrics a self contained approach: Multiple Time Series Models Patrick T. Brandt, John T. Williams, 2007 Many analyses of time series data involve multiple, related variables. Multiple Time Series Models presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.Key Features Offers a detailed comparison of different time series methods and approaches. Includes a self-contained introduction to vector autoregression modeling. Situates multiple time series modeling as a natural extension of commonly taught statistical models. |
| an introduction to econometrics a self contained approach: Introduction to Air Transport Economics Bijan Vasigh, Ken Fleming, 2016-05-06 Introduction to Air Transport Economics: From Theory to Applications uniquely merges the institutional and technical aspects of the aviation industry with their theoretical economic underpinnings. In one comprehensive textbook it applies economic theory to all aspects of the aviation industry, bringing together the numerous and informative articles and institutional developments that have characterized the field of airline economics in the last two decades as well as adding a number of areas original to an aviation text. Its integrative approach offers a fresh point of view that will find favor with many students of aviation. The book offers a self-contained theory and applications-oriented text for any individual intent on entering the aviation industry as a practicing professional in the management area. It will be of greatest relevance to undergraduate and graduate students interested in obtaining a more complete understanding of the economics of the aviation industry. It will also appeal to many professionals who seek an accessible and practical explanation of the underlying economic forces that shape the industry. The second edition has been extensively updated throughout. It features new coverage of macroeconomics for managers, expanded analysis of modern revenue management and pricing decisions, and also reflects the many significant developments that have occurred since the original’s publication. Instructors will find this modernized edition easier to use in class, and suitable to a wider variety of undergraduate or graduate course structures, while industry practitioners and all readers will find it more intuitively organized and more user friendly. |
| an introduction to econometrics a self contained approach: Impact Evaluation Markus Fröhlich, Markus Frölich, Stefan Sperlich, 2019-03-21 Encompasses the main concepts and approaches of quantitative impact evaluations, used to consider the effectiveness of programmes, policies, projects or interventions. This textbook for economics graduate courses can also serve as a manual for professionals in research institutes, governments, and international organizations. |
| an introduction to econometrics a self contained approach: Schaum's Outline of Statistics and Econometrics Dominick Salvatore, Derrick Reagle, 2001-10-22 Tough Test Questions? Missed Lectures? Not Enough Time? Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives you Practice problems with full explanations that reinforce knowledge Coverage of the most up-to-date developments in your course field In-depth review of practices and applications Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores! Schaum's Outlines-Problem Solved. |
| an introduction to econometrics a self contained approach: Handbook of Cliometrics Claude Diebolt, Michael Haupert, 2024-05-22 This handbook is a milestone in the field of historical economics and econometric history through its emphasis on the concrete contribution of cliometrics to our knowledge in economics and history. The articles in the handbook authored by the leading scholars in the fields, stress the usefulness of cliometrics for economists, historians and social scientists in general. The handbook offers a comprehensive coverage of topics with each article providing an overview of the contributions of cliometrics to a particular topic. The handbook has set a new standard of quality in the field by offering a world-wide forum of discussion in cliometrics. This updated and extended third edition of the handbook of cliometrics offers a substantially enlarged collection of articles and thus stresses its unique position as authoritative reference work in this field. |
| an introduction to econometrics a self contained approach: Econometric Theory and Methods Russell Davidson, 2009-04-30 Econometric Theory and Methods International Edition provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation. |
| an introduction to econometrics a self contained approach: Time Series and Panel Data Econometrics M. Hashem Pesaran, 2015 The book describes and illustrates many advances that have taken place in a number of areas in theoretical and applied econometrics over the past four decades. |
怎样写好英文论文的 Introduction 部分呢？ - 知乎
Introduction应该是一篇论文中最难写的一部分，也是最重要的。“A good introduction will “sell” the study to editors, reviewers, readers, and sometimes even the media.” [1]。通过Introduction可以 …
Difference between "introduction to" and "introduction of"
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