Search Results for "introduction-to-statistics-and-econometrics"

Introduction to Statistics and Econometrics

Introduction to Statistics and Econometrics

  • Author: Takeshi Amemiya
  • Publisher: Harvard University Press
  • ISBN: 9780674462250
  • Category: Business & Economics
  • Page: 368
  • View: 8412
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This outstanding text by a foremost econometrician combines instruction in probability and statistics with econometrics in a rigorous but relatively nontechnical manner. Unlike many statistics texts, it discusses regression analysis in depth. And unlike many econometrics texts, it offers a thorough treatment of statistics. Although its only mathematical requirement is multivariate calculus, it challenges the student to think deeply about basic concepts. The coverage of probability and statistics includes best prediction and best linear prediction, the joint distribution of a continuous and discrete random variable, large sample theory, and the properties of the maximum likelihood estimator. Exercises at the end of each chapter reinforce the many illustrative examples and diagrams. Believing that students should acquire the habit of questioning conventional statistical techniques, Takeshi Amemiya discusses the problem of choosing estimators and compares various criteria for ranking them. He also evaluates classical hypothesis testing critically, giving the realistic case of testing a composite null against a composite alternative. He frequently adopts a Bayesian approach because it provides a useful pedagogical framework for discussing many fundamental issues in statistical inference. Turning to regression, Amemiya presents the classical bivariate model in the conventional summation notation. He follows with a brief introduction to matrix analysis and multiple regression in matrix notation. Finally, he describes various generalizations of the classical regression model and certain other statistical models extensively used in econometrics and other applications in social science.

Introduction to the Mathematical and Statistical Foundations of Econometrics

Introduction to the Mathematical and Statistical Foundations of Econometrics

  • Author: Herman J. Bierens
  • Publisher: Cambridge University Press
  • ISBN: 9780521542241
  • Category: Business & Economics
  • Page: 323
  • View: 4966
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This book is intended for use in a rigorous introductory PhD level course in econometrics.

Introduction to Bayesian Econometrics

Introduction to Bayesian Econometrics

  • Author: Edward Greenberg
  • Publisher: Cambridge University Press
  • ISBN: 1107015316
  • Category: Business & Economics
  • Page: 249
  • View: 4335
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This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.

An Introduction to Econometric Theory

An Introduction to Econometric Theory

  • Author: James Davidson
  • Publisher: John Wiley & Sons
  • ISBN: 1119484928
  • Category: Business & Economics
  • Page: 256
  • View: 3608
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A guide to economics, statistics and finance that explores the mathematical foundations underling econometric methods An Introduction to Econometric Theory offers a text to help in the mastery of the mathematics that underlie econometric methods and includes a detailed study of matrix algebra and distribution theory. Designed to be an accessible resource, the text explains in clear language why things are being done, and how previous material informs a current argument. The style is deliberately informal with numbered theorems and lemmas avoided. However, very few technical results are quoted without some form of explanation, demonstration or proof. The author — a noted expert in the field — covers a wealth of topics including: simple regression, basic matrix algebra, the general linear model, distribution theory, the normal distribution, properties of least squares, unbiasedness and efficiency, eigenvalues, statistical inference in regression, t and F tests, the partitioned regression, specification analysis, random regressor theory, introduction to asymptotics and maximum likelihood. Each of the chapters is supplied with a collection of exercises, some of which are straightforward and others more challenging. This important text: Presents a guide for teaching econometric methods to undergraduate and graduate students of economics, statistics or finance Offers proven classroom-tested material Contains sets of exercises that accompany each chapter Includes a companion website that hosts additional materials, solution manual and lecture slides Written for undergraduates and graduate students of economics, statistics or finance, An Introduction to Econometric Theory is an essential beginner’s guide to the underpinnings of econometrics.

Introduction to Spatial Econometrics

Introduction to Spatial Econometrics

  • Author: James LeSage,Robert Kelley Pace
  • Publisher: CRC Press
  • ISBN: 9781420064254
  • Category: Mathematics
  • Page: 340
  • View: 9696
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Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances. Leaders in this field, the authors clarify the often-mystifying phenomenon of simultaneous spatial dependence. By presenting new methods, they help with the interpretation of spatial regression models, especially ones that include spatial lags of the dependent variable. The authors also examine the relationship between spatiotemporal processes and long-run equilibrium states that are characterized by simultaneous spatial dependence. MATLAB® toolboxes useful for spatial econometric estimation are available on the authors’ websites. This work covers spatial econometric modeling as well as numerous applied illustrations of the methods. It encompasses many recent advances in spatial econometric models—including some previously unpublished results.

An Introduction to Econometric Theory

An Introduction to Econometric Theory

Measure-Theoretic Probability and Statistics with Applications to Economics

  • Author: A. Ronald Gallant
  • Publisher: Princeton University Press
  • ISBN: 0691016453
  • Category: Business & Economics
  • Page: 202
  • View: 9961
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Intended primarily to prepare first-year graduate students for their ongoing work in econometrics, economic theory, and finance, this innovative book presents the fundamental concepts of theoretical econometrics, from measure-theoretic probability to statistics. A. Ronald Gallant covers these topics at an introductory level and develops the ideas to the point where they can be applied. He thereby provides the reader not only with a basic grasp of the key empirical tools but with sound intuition as well. In addition to covering the basic tools of empirical work in economics and finance, Gallant devotes particular attention to motivating ideas and presenting them as the solution to practical problems. For example, he presents correlation, regression, and conditional expectation as a means of obtaining the best approximation of one random variable by some function of another. He considers linear, polynomial, and unrestricted functions, and leads the reader to the notion of conditioning on a sigma-algebra as a means for finding the unrestricted solution. The reader thus gains an understanding of the relationships among linear, polynomial, and unrestricted solutions. Proofs of results are presented when the proof itself aids understanding or when the proof technique has practical value. A major text-treatise by one of the leading scholars in this field, An Introduction to Econometric Theory will prove valuable not only to graduate students but also to all economists, statisticians, and finance professionals interested in the ideas and implications of theoretical econometrics.

An Introduction to Econometrics

An Introduction to Econometrics

A Self-contained Approach

  • Author: Frank Westhoff
  • Publisher: Mit Press
  • ISBN: 9780262019224
  • Category: Business & Economics
  • Page: 874
  • View: 6216
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An introductory textbook (requiring no previous knowledge of probability and statistics) that offers students a solid foundation in regression analysis. This unique introduction to econometrics provides undergraduate students with a command of regression analysis in one semester, enabling them to grasp the empirical literature and undertake serious quantitative projects of their own. It does not assume any previous exposure to probability and statistics but does discuss the concepts in these areas that are essential for econometrics. The bulk of the textbook is devoted to regression analysis, from simple to advanced topics. Students will gain an intuitive understanding of the mathematical concepts; Java applet simulations on the book's website demonstrate how the algebraic equations are derived in the text and are designed to reinforce the important concepts. After presenting the essentials of probability and statistics, the book covers simple regression analysis, multiple regression analysis, and advanced topics including heteroskedasticity, autocorrelation, large sample properties, instrumental variables, measurement error, omitted variables, panel data, simultaneous equations, and binary/truncated dependent variables. Two optional chapters treat additional probability and statistics topics. Each chapter offers examples, prep problems (bringing students "up to speed" at the beginning of a chapter), review questions, and exercises. An accompanying website offers students easy access to Java simulations and data sets (available in EViews, Stata, and Excel files). After a single semester spent mastering the material presented in this book, students will be prepared to take any of the many elective courses that use econometric techniques. * Requires no background in probability and statistics * Regression analysis focus * "Econometrics lab" with Java applet simulations on accompanying Website

A Concise Introduction to Econometrics

A Concise Introduction to Econometrics

An Intuitive Guide

  • Author: Philip Hans Franses
  • Publisher: Cambridge University Press
  • ISBN: 9780521520904
  • Category: Business & Economics
  • Page: 117
  • View: 9264
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This 2002 book is an ideal practical introduction to the basics of econometrics.

Introduction to the Theory and Practice of Econometrics

Introduction to the Theory and Practice of Econometrics

  • Author: George G. Judge
  • Publisher: John Wiley & Sons
  • ISBN: N.A
  • Category: Business & Economics
  • Page: 839
  • View: 9738
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Foundations: statistical model specification, estimation, and inferencce; The general linear statistical model; The generalized linear statistical model; Simultaneous linear statistical models; Some procedures for handling an unknown covariance matrix; Pooling of data and varying parameter models; Unobservable and qualitative variables; Nonsample information, biased estimation and choosing the dimension and form of the design matrix; The nonlinear statistical model; Time series and distributed lag models.

Introduction to Statistical Time Series

Introduction to Statistical Time Series

  • Author: Wayne A. Fuller,J.K. Watson,Wayne Arthur Fuller
  • Publisher: John Wiley & Sons
  • ISBN: 9780471552390
  • Category: Mathematics
  • Page: 698
  • View: 5801
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The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter. Major topics include: Moving average and autoregressive processes Introduction to Fourier analysis Spectral theory and filtering Large sample theory Estimation of the mean and autocorrelations Estimation of the spectrum Parameter estimation Regression, trend, and seasonality Unit root and explosive time series To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.

Introduction to Econometrics

Introduction to Econometrics

  • Author: Christopher Dougherty
  • Publisher: Oxford University Press
  • ISBN: 0199567085
  • Category: Business & Economics
  • Page: 573
  • View: 3869
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Taking a modern approach to the subject, this text provides students with a solid grounding in econometrics, using non-technical language wherever possible.

Mathematical Statistics for Economics and Business

Mathematical Statistics for Economics and Business

  • Author: Ron C. Mittelhammer
  • Publisher: Springer Science & Business Media
  • ISBN: 1461450225
  • Category: Mathematics
  • Page: 755
  • View: 1985
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Mathematical Statistics for Economics and Business, Second Edition, provides a comprehensive introduction to the principles of mathematical statistics which underpin statistical analyses in the fields of economics, business, and econometrics. The selection of topics in this textbook is designed to provide students with a conceptual foundation that will facilitate a substantial understanding of statistical applications in these subjects. This new edition has been updated throughout and now also includes a downloadable Student Answer Manual containing detailed solutions to half of the over 300 end-of-chapter problems. After introducing the concepts of probability, random variables, and probability density functions, the author develops the key concepts of mathematical statistics, most notably: expectation, sampling, asymptotics, and the main families of distributions. The latter half of the book is then devoted to the theories of estimation and hypothesis testing with associated examples and problems that indicate their wide applicability in economics and business. Features of the new edition include: a reorganization of topic flow and presentation to facilitate reading and understanding; inclusion of additional topics of relevance to statistics and econometric applications; a more streamlined and simple-to-understand notation for multiple integration and multiple summation over general sets or vector arguments; updated examples; new end-of-chapter problems; a solution manual for students; a comprehensive answer manual for instructors; and a theorem and definition map. This book has evolved from numerous graduate courses in mathematical statistics and econometrics taught by the author, and will be ideal for students beginning graduate study as well as for advanced undergraduates.

Applied Econometrics with R

Applied Econometrics with R

  • Author: Christian Kleiber,Achim Zeileis
  • Publisher: Springer Science & Business Media
  • ISBN: 9780387773186
  • Category: Business & Economics
  • Page: 222
  • View: 9548
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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.

The Cartoon Introduction to Statistics

The Cartoon Introduction to Statistics

  • Author: Grady Klein,Alan Dabney
  • Publisher: Hill and Wang
  • ISBN: 9780809033669
  • Category: Mathematics
  • Page: 240
  • View: 1638
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The Cartoon Introduction to Statistics is the most imaginative and accessible introductory statistics course you'll ever take. Employing an irresistible cast of dragon-riding Vikings, lizard-throwing giants, and feuding aliens, the renowned illustrator Grady Klein and the award-winning statistician Alan Dabney teach you how to collect reliable data, make confident statements based on limited information, and judge the usefulness of polls and the other numbers that you're bombarded with every day. If you want to go beyond the basics, they've created the ultimate resource: "The Math Cave," where they reveal the more advanced formulas and concepts. Timely, authoritative, and hilarious, The Cartoon Introduction to Statistics is an essential guide for anyone who wants to better navigate our data-driven world.

An Introduction to Statistical Learning

An Introduction to Statistical Learning

with Applications in R

  • Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
  • Publisher: Springer Science & Business Media
  • ISBN: 1461471389
  • Category: Mathematics
  • Page: 426
  • View: 4621
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Computational Methods in Statistics and Econometrics

Computational Methods in Statistics and Econometrics

  • Author: Hisashi Tanizaki
  • Publisher: CRC Press
  • ISBN: 0824750888
  • Category: Mathematics
  • Page: 528
  • View: 8453
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Reflecting current technological capacities and analytical trends, Computational Methods in Statistics and Econometrics showcases Monte Carlo and nonparametric statistical methods for models, simulations, analyses, and interpretations of statistical and econometric data. The author explores applications of Monte Carlo methods in Bayesian estimation, state space modeling, and bias correction of ordinary least squares in autoregressive models. The book offers straightforward explanations of mathematical concepts, hundreds of figures and tables, and a range of empirical examples. A CD-ROM packaged with the book contains all of the source codes used in the text.

Intermediate Statistics and Econometrics

Intermediate Statistics and Econometrics

A Comparative Approach

  • Author: Dale J. Poirier
  • Publisher: MIT Press
  • ISBN: 9780262161497
  • Category: Business & Economics
  • Page: 715
  • View: 3234
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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 Challenger space 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 Financial Econometrics

An Introduction to Financial Econometrics

  • Author: Oliver B. Linton
  • Publisher: Blackwell Pub
  • ISBN: 9781405107648
  • Category: Business & Economics
  • Page: 300
  • View: 7580
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Building upon a basic understanding of econometrics and statistics towards the models and estimation techniques of financial econometrics, this text covers topics such as models for volatility and high frequency data, static and dynamic yield curve models and value at risk.

An Introduction to Modern Econometrics Using Stata

An Introduction to Modern Econometrics Using Stata

  • Author: Christopher F. Baum
  • Publisher: Stata Press
  • ISBN: 1597180130
  • Category: Business & Economics
  • Page: 341
  • View: 8869
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Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata. As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent variables and the two appendices discuss how to import data into Stata and Stata programming. Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts.