[下载]全套伍德里奇wooldridge初级和高级教材电子书+数据集+答案

>> Home >> Bookstore >> Statistics >> Econometrics >> Introductory Econometrics: A Modern Approach, 4th Edition

Introductory Econometrics: A Modern Approach, 4th Edition Author: Jeffrey M. Wooldridge Publisher: South-Western Copyright: 2008 ISBN-10: 0-324-58162-9 ISBN-13: 978-0-324-58162-1 Pages: 912; hardcover Price: $135.00 See a large photo of the front cover See the back cover Table of contents Comment from the Stata technical group The fourth edition of Jeffrey Wooldridge’s textbook, Introductory Econometrics: A Modern Approach, lives up to its subtitle. This text exemplifies a modern approach to econometrics in its choice of topics and its treatment of standard material. Wooldridge recognizes that modern econometrics involves much more than ordinary least squares (OLS) with a few extensions to handle the special cases commonly encountered in econometric data. In addition to chapters on OLS, he includes chapters on current techniques of estimation and inference for time-series data, panel data, limited dependent variables, and sample selection. In his treatments of standard OLS and two-stage least squares, Wooldridge breaks new ground by concentrating on advanced statistical concepts instead of matrix algebra. A traditional approach to introductory econometrics would use advanced materials sections to give courses on matrix algebra and its applications in econometrics. In contrast, Wooldridge uses the advanced sections of his text to introduce recently developed statistical concepts and techniques. This approach leads to a text with greater breadth than is usual in books of this type. This book is equally useful for advanced undergraduate study, as the basis of a survey course at the graduate level, or as a conceptual supplement to advanced courses. The fourth edition contains new material on the smearing estimator after log-linear OLS, expanded treatments of least-absolute-deviations estimators, linear models for heteroskedastic data, Chow tests for panel data, and panel-data methods for cluster samples. The result is that an excellent introductory book has been made even better. Table of contents Chapter 1 The Nature of Econometrics and Economic Data 1.1 What is Econometrics? 1.2 Steps in Empirical Economic Analysis 1.3 The Structure of Economic Data Cross-Sectional Data Time Series Data Pooled Cross Sections Panel or Longitudinal Data A Comment on Data Structures 1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis Summary Key Terms Computer Exercises Part 1 Regression Analysis with Cross-Sectional Data Chapter 2 The Simple Regression Model 2.1 Definition of the Simple Regression Model 2.2 Deriving the Ordinary Least Squares Estimates A Note on Terminology 2.3 Properties of OLS on Any Sample of Data Fitted Values and Residuals Algebraic Properties of OLS Statistics Goodness-of-Fit 2.4 Units of Measurement and Functional Form The Effects of Changing Units of Measurement on OLS Statistics Incorporating Nonlinearities in Simple Regression The Meaning of "Linear" Regression 2.5 Expected Values and Variances of the OLS Estimators Unbiasedness of OLS Variances of the OLS Estimators Estimating the Error Variance 2.6 Regression Through the Origin Summary Key Terms Problems Computer Exercises Appendix 2A Chapter 3 Multiple Regression Analysis: Estimation 3.1 Motivation for Multiple Regression The Model with Two Independent Variables The Model with k Independent Variables 3.2 Mechanics and Interpretation of Ordinary Least Squares Obtaining the OLS Estimates Interpreting the OLS Regression Equation On the Meaning of "Holding Other Factors Fixed" in Multiple Regression Changing More than One Independent Variable Simultaneously OLS Fitted Values and Residuals A "Partialling Out" Interpretation of Multiple Regression Comparison of Simple and Multiple Regression Estimates Goodness-of-Fit Regression Through the Origin 3.3 The Expected Value of the OLS Estimators Including Irrelevant Variable in a Regression Model Omitted Variable Bias: The Simple Case Omitted Variable Bias: More General Cases 3.4 The Variance of the OLS Estimators The Components of the OLS Variances: Multicollinearity Variances in Misspecified Models Estimating σ2: Standard Errors of the OLS Estimators 3.5 Efficiency of OLS: The Gauss-Markov Theorem Summary Key Terms Problems Computer Exercises Appendix 3A Chapter 4 Multiple Regression Analysis: Inference 4.1 Sampling Distributions of the OLS Estimators 4.2 Testing Hypotheses About a Single Population Parameter: The t Test Testing Against One-Sided Alternatives Two-Sided Alternatives Testing Other Hypotheses About βj Computing p-values for t tests A Reminder on the Language of Classical Hypothesis Testing Economic, or Practical, versus Statistical Significance 4.3 Confidence Intervals 4.4 Testing Hypotheses About a Single Linear Combination of the Parameters 4.5 Testing Multiple Linear Restrictions: The F Test Testing Exclusion Restrictions Relationship Between F and t Statistics The R-Squared Form of the F Statistic Computing p-values for F Tests The F Statistic for Overall Significance of a Regression Testing General Linear Restrictions 4.6 Reporting Regression Results Summary Key Terms Problems Computer Exercises Chapter 5 Multiple Regression Analysis: OLS Asymptotics 5.1 Consistency Deriving the Inconsistency in OLS 5.2 Asymptotic Normality and Large Sample Inference Other Large Sample Tests: The Lagrange Multiplier Statistic 5.3 Asymptotic Efficiency of OLS Summary Key Terms Problems Computer Exercises Appendix 5A Chapter 6 Multiple Regression Analysis: Further Issues 6.1 Effects of Data Scaling on OLS Statistics Beta Coefficients 6.2 More on Functional Form More on Using Logarithmic Functional Forms Models with Quadratics Models with Interaction Terms 6.3 More on Goodness-of-Fit and Selection of Regressors Adjusted R-Squared Using Adjusted R-Squared to Choose Between Nonnested Models Controlling for Too Many Factors in Regression Analysis Adding Regressors to Reduce the Error Variance 6.4 Prediction and Residual Analysis Confidence Intervals for Predictions Residual Analysis Predicting y When log(y) Is the Dependent Variable Summary Key Terms Problems Computer Exercises Appendix 6A Chapter 7 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 7.1 Describing Qualitative Information 7.2 A Single Dummy Independent Variable Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable is log(y) 7.3 Using Dummy Variables for Multiple Categories Incorporating Ordinal Information by Using Dummy Variables 7.4 Interactions Involving Dummy Variables Interactions Among Dummy Variables Allowing for Different Slopes Testing for Differences in Regression Functions Across Groups 7.5 A Binary Dependent Variable: The Linear Probability Model 7.6 More on Policy Analysis And Program Evaluation Summary Key Terms Problems Computer Exercises Chapter 8 Heteroskedasticity 8.1 Consequences of Heteroskedasticity for OLS 8.2 Heteroskedasticity-Robust Inference After OLS Estimation Computing Heteroskedasticity-Robust LM Tests 8.3 Testing for Heteroskedasticity The White Test for Heteroskedasticity 8.4 Weighted Least Squares Estimation The Heteroskedasticity Is Known up to a Multiplicative Constant The Heteroskedasticity Function Must Be Estimated: Feasible GLS What If the Assumed Heteroskedasticity Function is Wrong? Prediction and Prediction Intervals with Heteroskedasticity 8.5 The Linear Probability Model Revisited Summary Key Terms Problems Computer Exercises Chapter 9 More on Specification and Data Problems 9.1 Functional Form Misspecification RESET as a General Test for Functional Form Misspecification Tests Against Nonnested Alternatives 9.2 Using Proxy Variables for Unobserved Explanatory Variables Using Lagged Dependent Variables as Proxy Variables A Different Slant on Multiple Regression 9.3 Models with Randm Slopes 9.4 Properties of OLS Under Measurement Error Measurement Error in the Dependent Variable Measurement Error in an Explanatory Variable 9.5 Missing Data, Nonrandom Samples, and Outlying Observations 9.6 Least Absolute Deviations Estimation Missing Data Nonrandom Samples Outlying and Influential Observations Summary Key Terms Problems Computer Exercises Part 2 Regression Analysis with Time Series Data Chapter 10 Basic Regression Analysis with Time Series Data 10.1 The Nature of Time Series Data 10.2 Examples of Time Series Regression Models Static Models Finite Distributed Lag Models A Convention About the Time Index 10.3 Finite Sample Properties of OLS Under Classical Assumptions Unbiasedness of OLS The Variances of the OLS Estimators and the Gauss-Markov Theorem Inference Under the Classical Linear Model Assumptions 10.4 Functional Form, Dummy Variables, and Index Numbers 10.5 Trends and Seasonality Characterizing Trending Time Series Using Trending Variables in Regression Analysis A Detrending Interpretation of Regressions with a Time Trend Computing R-squared When the Dependent Variable Is Trending Seasonality Summary Key Terms Problems Computer Exercises Chapter 11 Further Issues in Using OLS with Time Series Data 11.1 Stationary and Weakly Dependent Time Series Stationary and Nonstationary Time Series Weakly Dependent Time Series 11.2 Asymptotic Properties of OLS 11.3 Using Highly Persistent Time Series in Regression Analysis Highly Persistent Time Series Transformations on Highly Persistent Time Series Deciding Whether a Time Series is I(1) 11.4 Dynamically Complete Models and the Absence of Serial Correlation 11.5 The Homoskedasticity Assumption for Time Series Models Summary Key Terms Problems Computer Exercises Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 12.1 Properties of OLS with Serially Correlated Errors Unbiasedness and Consistency Efficiency and Inference Goodness-of-Fit Serial Correlation in the Presence of Lagged Dependent Variables 12.2 Testing for Serial Correlation A t test for AR(1) Serial Correlation with Strictly Exogenous Regressors The Durbin–Watson Test under Classical Assumptions Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors Testing for Higher Order Serial Correlation 12.3 Correcting for Serial Correlation with Strictly Exogenous Regressors Obtaining the Best Linear Unbiased Estimator in the AR(1) Model Feasible GLS Estimation with AR(1) Errors Comparing OLS and FGLS Correcting for Higher Order Serial Correlation 12.4 Differencing and Serial Correlation 12.5 Serial Correlation-Robust Inference After OLS 12.6 Heteroskedasticity in Time Series Regressions Heteroskedasticity-Robust Statistics Testing for Heteroskedasticity Autoregressive Conditional Heteroskedasticity Heteroskedasticity and Serial Correlation in Regression Models Summary Key Terms Problems Computer Exercises Part 3 Advanced Topics Chapter 13 Pooling Cross Sections Across Time: Simple Panel Data Methods 13.1 Pooling Independent Cross Sections Across Time The Chow Test for Structural Change Across Time 13.2 Policy Analysis with Pooled Cross Sections 13.3 Two-Period Panel Data Analysis Organizing Panel Data 13.4 Policy Analysis with Two-Period Panel Data 13.5 Differencing with More than Two Time Periods Potential Pitfalls in First-Differencing Panel Data Summary Key Terms Problems Computer Exercises Appendix 13A Chapter 14 Advanced Panel Data Methods 14.1 Fixed Effects Estimation The Dummy Variable Regression Fixed Effects or First Differencing? Fixed Effects with Unbalanced Panels 14.2 Random Effects Models Random Effects or Fixed Effects? 14.3 Applying Panel Data Methods to Other Data Structures Summary Key Terms Problems Computer Exercises Appendix 14A Chapter 15 Instrumental Variables Estimation and Two Stage Least Squares 15.1 Motivation: Omitted Variables in a Simple Regression Model Statistical Inference with the IV Estimator Properties of IV with a Poor Instrumental Variable Computing R-Squared After IV Estimation 15.2 IV Estimation of the Multiple Regression Model 15.3 Two Stage Least Squares A Single Endogenous Explanatory Variable Multicollinearity and 2SLS Multiple Endogenous Explanatory Variables Testing Multiple Hypotheses After 2SLS Estimation 15.4 IV Solutions to Errors-in-Variables Problems 15.5 Testing for Endogeneity and Testing Overidentifying Restrictions Testing for Endogeneity Testing Overidentification Restrictions 15.6 2SLS with Heteroskedasticity 15.7 Applying 2SLS to Time Series Equations 15.8 Applying 2SLS to Pooled Cross Sections and Panel Data Summary Key Terms Problems Computer Exercises Appendix 15A Chapter 16 Simultaneous Equations Models 16.1 The Nature of Simultaneous Equations Models 16.2 Simultaneity Bias in OLS 16.3 Identifying and Estimating a Structural Equation Identification in a Two-Equation System Estimation by 2SLS 16.4 Systems with More than Two Equations Identification in Systems with Three or More Equations Estimation 16.5 Simultaneous Equations Models with Time Series 16.6 Simultaneous Equations Models with Panel Data Summary Key Terms Problems Computer Exercises Chapter 17 Limited Dependent Variable Models and Sample Selection Corrections 17.1 Logit and Probit Models for Binary Response Specifying Logit and Probit Models Maximum Likelihood Estimation of Logit and Probit Models Testing Multiple Hypotheses Interpreting the Logit and Probit Estimates 17.2 The Tobit Model for Corner Solution Responses Interpreting the Tobit Estimates Specification Issues in Tobit Models 17.3 The Poisson Regression Model 17.4 Censored and Truncated Regression Models Censored Regression Models Truncated Regression Models 17.5 Sample Selection Corrections When is OLS on the Selected Sample Consistent? Incidental Truncation Summary Key Terms Problems Computer Exercises Appendix 17A Appendix 17B Chapter 18 Advanced Time Series Topics 18.1 Infinite Distributed Lag Models The Geometric (or Koyck) Distributed Lag Rational Distributed Lag Models 18.2 Testing for Unit Roots 18.3 Spurious Regression 18.4 Cointegration and Error Correction Models Cointegration Error Correction Models 18.5 Forecasting Types of Regression Models Used for Forecasting One-Step-Ahead Forecasting Comparing One-Step-Ahead Forecasts Multiple-Step-Ahead Forecasts Forecasting Trending, Seasonal, and Integrated Processes Summary Key Terms Problems Computer Exercises Chapter 19 Carrying Out an Empirical Project 19.1 Posing a Question 19.2 Literature Review 19.3 Data Collection Deciding on the Appropriate Data Set Entering and Storing Your Data Inspecting, Cleaning, and Summarizing Your Data 19.4 Econometric Analysis 19.5 Writing an Empirical Paper Introduction Conceptual (or Theoretical) Framework Econometric Models and Estimation Methods The Data Results Conclusions Style Hints Summary Key Terms Sample Empirical Projects List of Journals Data Sources Appendices Appendix A Basic Mathematical Tools A.1 The Summation Operator and Descriptive Statistics A.2 Properties of Linear Functions A.3 Proportions and Percentages A.4 Some Special Functions and Their Properties Quadratic Functions The Natural Logarithm The Exponential Function A.5 Differential Calculus Summary Key Terms Problems Appendix B Fundamentals of Probability B.1 Random Variables and Their Probability Distributions Discrete Random Variables Continuous Random Variables B.2 Joint Distributions, Conditional Distributions, and Independence Joint Distributions and Independence Conditional Distributions B.3 Features of Probability Distributions A Measure of Central Tendency: The Expected Value Properties of Expected Value Another Measure of Central Tendency: The Median Measures of Variability: Variance and Standard Deviation Variance Standard Deviation Standardizing a Random Variable B.4 Features of Joint and Conditional Distributions Measures of Association: Covariance and Correlation Covariance Correlation Coefficient Variance of Sums of Random Variables Conditional Expectation Properties of Conditional Expectation Conditional Variance B.5 The Normal and Related Distributions The Normal Distribution The Standard Normal Distribution Additional Properties of the Normal Distribution The Chi-Square Distribution The t Distribution The F Distribution Summary Key Terms Problems Appendix C Fundamentals of Mathematical Statistics C.1 Populations, Parameters, and Random Sampling Sampling C.2 Finite Sample Properties of Estimators Estimators and Estimates Unbiasedness The Sampling Variance of Estimators Efficiency C.3 Asymptotic or Large Sample Properties of Estimators Consistency Asymptotic Normality C.4 General Approaches to Parameter Estimation Method of Moments Maximum Likelihood Least Squares C.5 Interval Estimation and Confidence Intervals The Nature of Interval Estimation Confidence Intervals for the Mean from a Normally Distributed Population A Simple Rule of Thumb for a 95% Confidence Interval Asymptotic Confidence Intervals for Nonnormal Populations C.6 Hypothesis Testing Fundamentals of Hypothesis Testing Testing Hypotheses About the Mean in a Normal Population Asymptotic Tests for Nonnormal Populations Computing and Using p-Values The Relationship Between Confidence Intervals and Hypothesis Testing Practical Versus Statistical Significance C.7 Remarks on Notation Summary Key Terms Problems Appendix D Summary of Matrix Algebra D.1 Basic Definitions D.2 Matrix Operations Matrix Addition Scalar Multiplication Matrix Multiplication Transpose Partitioned Matrix Multiplication Trace Inverse D.3 Linear Independence. Rank of a Matrix D.4 Quadratic Forms and Positive Definite Matrices D.5 Idempotent Matrices D.6 Differentiation of Linear and Quadratic Forms D.7 Moments and Distributions of Random Vectors Expected Value Variance-Covariance Matrix Multivariate Normal Distribution Chi-Square Distribution t Distribution F Distribution Summary Key Terms Problems Appendix E The Linear Regression Model in Matrix Form E.1 The Model and Ordinary Least Squares Estimation E.2 Finite Sample Properties of OLS E.3 Statistical Inference E.4 Some Asymptotic Analysis Summary Key Terms Problems Appendix F Answers to Chapter Questions Appendix G Statistical Tables References Glossary Index

楼主真是好人啊

谢谢楼主,支持

thanks so much

谢谢

哦

[此贴子已经被作者于2008-9-6 1:59:39编辑过]

好同志

thanks

已经下载，很好，很全面！谢谢楼主！！

楼主真是好人！