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: 6249
DOWNLOAD NOW »
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: 9923
DOWNLOAD NOW »
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: 3072
DOWNLOAD NOW »
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.

Introduction to Spatial Econometrics

Introduction to Spatial Econometrics

  • Author: James LeSage,Robert Kelley Pace
  • Publisher: CRC Press
  • ISBN: 9781420064254
  • Category: Mathematics
  • Page: 340
  • View: 9964
DOWNLOAD NOW »
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

  • Author: James Davidson
  • Publisher: John Wiley & Sons
  • ISBN: 1119484928
  • Category: Business & Economics
  • Page: 256
  • View: 4358
DOWNLOAD NOW »
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.

An Introduction to Econometrics

An Introduction to Econometrics

A Self-contained Approach

  • Author: Frank Westhoff
  • Publisher: MIT Press (MA)
  • ISBN: 9780262019224
  • Category: Business & Economics
  • Page: 874
  • View: 1583
DOWNLOAD NOW »
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

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: 0691186235
  • Category: Business & Economics
  • Page: N.A
  • View: 4484
DOWNLOAD NOW »
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.

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: 2195
DOWNLOAD NOW »
This 2002 book is an ideal practical introduction to the basics of econometrics.

Economic statistics and econometrics

Economic statistics and econometrics

an introduction to quantitative economics

  • Author: Edward James Kane
  • Publisher: N.A
  • ISBN: N.A
  • Category: Econometrics
  • Page: 437
  • View: 9861
DOWNLOAD NOW »

Probability, Statistics and Econometrics

Probability, Statistics and Econometrics

  • Author: Oliver Linton
  • Publisher: Academic Press
  • ISBN: 0128104961
  • Category: Business & Economics
  • Page: 388
  • View: 4815
DOWNLOAD NOW »
Probability, Statistics and Econometrics provides a concise, yet rigorous, treatment of the field that is suitable for graduate students studying econometrics, very advanced undergraduate students, and researchers seeking to extend their knowledge of the trinity of fields that use quantitative data in economic decision-making. The book covers much of the groundwork for probability and inference before proceeding to core topics in econometrics. Authored by one of the leading econometricians in the field, it is a unique and valuable addition to the current repertoire of econometrics textbooks and reference books. Synthesizes three substantial areas of research, ensuring success in a subject matter than can be challenging to newcomers Focused and modern coverage that provides relevant examples from economics and finance Contains some modern frontier material, including bootstrap and lasso methods not treated in similar-level books Collects the necessary material for first semester Economics PhD students into a single text

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: 6521
DOWNLOAD NOW »
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.

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: 7724
DOWNLOAD NOW »
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.

Introduction to Econometrics

Introduction to Econometrics

  • Author: Christopher Dougherty
  • Publisher: Oxford University Press
  • ISBN: 0199567085
  • Category: Business & Economics
  • Page: 573
  • View: 2351
DOWNLOAD NOW »
Taking a modern approach to the subject, this text provides students with a solid grounding in econometrics, using non-technical language wherever possible.

Introduction to Statistics

Introduction to Statistics

Using Interactive MM*Stat Elements

  • Author: Wolfgang Karl Härdle,Sigbert Klinke,Bernd Rönz
  • Publisher: Springer
  • ISBN: 3319177044
  • Category: Business & Economics
  • Page: 516
  • View: 3791
DOWNLOAD NOW »
MM*Stat, together with its enhanced online version with interactive examples, offers a flexible tool that facilitates the teaching of basic statistics. It covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing). MM*Stat is also designed to help students rework class material independently and to promote comprehension with the help of additional examples. Each chapter starts with the necessary theoretical background, which is followed by a variety of examples. The core examples are based on the content of the respective chapter, while the advanced examples, designed to deepen students’ knowledge, also draw on information and material from previous chapters. The enhanced online version helps students grasp the complexity and the practical relevance of statistical analysis through interactive examples and is suitable for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields, e.g. economics, the social sciences etc. All R codes and data sets may be downloaded via the quantlet download center

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: 5566
DOWNLOAD NOW »
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.

Introduction to Econometrics

Introduction to Econometrics

  • Author: James H. Stock,Mark W. Watson
  • Publisher: Addison-Wesley Longman
  • ISBN: 9780321432513
  • Category: Business & Economics
  • Page: 379
  • View: 4817
DOWNLOAD NOW »
In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that readers apply the theory immediately. Introduction to Econometrics, Brief, is a streamlined version of their text, including the fundamental topics, an early review of statistics and probability, the core material of regression with cross-sectional data, and a capstone chapter on conducting empirical analysis. Introduction and Review: Economic Questions and Data; Review of Probability; Review of Statistics. Fundamentals of Regression Analysis: Linear Regression with One Regressor; Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals in the Single-Regressor Model; Linear Regression with Multiple Regressors; Hypothesis Tests and Confidence Intervals in the Multiple Regressor Model; Nonlinear Regression Functions; Assessing Studies Based on Multiple Regression; Conducting a Regression Study Using Economic Data. MARKET: For all readers interested in econometrics.

An Introduction to Analysis of Financial Data with R

An Introduction to Analysis of Financial Data with R

  • Author: Ruey S. Tsay
  • Publisher: John Wiley & Sons
  • ISBN: 1119013461
  • Category: Business & Economics
  • Page: 416
  • View: 3816
DOWNLOAD NOW »
A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

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: 4583
DOWNLOAD NOW »
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.