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Introductory Econometrics A Modern Approach 5th Edition by Jeffrey M Wooldridge ISBN 1111531048 9781111531041

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Introductory Econometrics A Modern Approach 5th Edition Jeffrey M. Wooldridge Digital Instant Download

Author(s): Jeffrey M. Wooldridge
ISBN(s): 9781111531041, 1111531048
Edition: 5
File Details: PDF, 9.46 MB
Year: 2012
Language: english
SKU: EB-4177804 Category: Tag:

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ISBN 10: 1111531048 
ISBN 13: 9781111531041
Author: Jeffrey M Wooldridge

Discover how empirical researchers today actually think about and apply econometric methods with the practical, professional approach in Wooldridge�s INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 5E. Unlike traditional books on the subject, INTRODUCTORY ECONOMETRICS� unique presentation demonstrates how econometrics has moved beyond just a set of abstract tools to become a genuinely useful tool for answering questions in business, policy evaluation, and forecasting environments. Organized around the type of data being analyzed, the book uses a systematic approach that only introduces assumptions as they are needed, which makes the material easier to understand and ultimately leads to better econometric practices. Packed with timely, relevant applications, the text emphasizes incorporates close to 100 intriguing data sets in six formats and offers updates that reflect the latest emerging developments in the field.

Introductory Econometrics A Modern Approach 5th 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
    • 1.3a Cross-Sectional Data

    • 1.3b Time Series Data
    • 1.3c Pooled Cross Sections
    • 1.3d Panel or Longitudinal Data
    • 1.3e A Comment on Data Structures
  • 1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis

  • Summary
  • Key Terms
  • Problems
  • 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

  • 2.3 Properties of OLS on Any Sample of Data
  • 2.4 Units of Measurement and Functional Form
  • 2.5 Expected Values and Variances of the OLS Estimators

  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 3: Multiple Regression Analysis: Estimation

  • 3.1 Motivation for Multiple Regression

  • 3.2 Mechanics and Interpretation of Ordinary Least Squares
  • 3.3 The Expected Value of the OLS Estimators
  • 3.4 The Variance of the OLS Estimators

  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

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

  • 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
  • 4.6 Reporting Regression Results

  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 5: Multiple Regression Analysis: OLS Asymptotics

  • 5.1 Consistency
  • 5.2 Asymptotic Normality and Large Sample Inference
  • 5.3 Asymptotic Efficiency of OLS
  • 5.4 Testing Normality of the Error Term
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 6: Multiple Regression Analysis: Further Issues

 

  • 6.1 Data Scaling and Dummy Variables
  • 6.2 More on Functional Form
  • 6.3 Goodness-of-Fit and Selection of Regressors
  • 6.4 Predicting y When the Dependent Variable Is log(y)
  • 6.5 Adding Interaction Terms
  • 6.6 More on Specification and Data Issues
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 7: Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables

  • 7.1 Describing Qualitative Information

  • 7.2 A Single Dummy Independent Variable
  • 7.3 Using Dummy Variables for Multiple Categories
  • 7.4 Interactions Involving Dummy Variables
  • 7.5 A Binary Dependent Variable: The Linear Probability Model

  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 8: Heteroskedasticity

  • 8.1 Consequences of Heteroskedasticity for OLS

  • 8.2 Detecting Heteroskedasticity
  • 8.3 Weighted Least Squares Estimation
  • 8.4 The White Robust Standard Errors
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 9: More on Specification and Data Issues

  • 9.1 Functional Form Misspecification
  • 9.2 Proxy Variables

  • 9.3 Measurement Error
  • 9.4 Missing Data, Nonrandom Sampling, and Outliers
  • 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

  • 10.3 Basic Properties of OLS with Time Series Data
  • 10.4 Functional Form, Dummy Variables, and Indexing
  • 10.5 Trends and Seasonality
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 11: Further Issues in Using OLS with Time Series Data

  • 11.1 Stationarity and Weakly Dependent Time Series

  • 11.2 Asymptotic Properties of OLS Under Weak Dependence
  • 11.3 Using Highly Persistent Time Series in Regression Analysis
  • 11.4 Dynamic Models and Lagged Explanatory Variables
  • 11.5 Granger Causality and Vector Autoregressions
  • 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

  • 12.2 Detecting Serial Correlation
  • 12.3 Correcting for Serial Correlation
  • 12.4 Heteroskedasticity in Time Series Regressions
  • 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

  • 13.2 Two-Period Panel Data Analysis
  • 13.3 Differencing with More Than Two Time Periods
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 14: Advanced Panel Data Methods

  • 14.1 Fixed Effects Estimation
  • 14.2 Random Effects Models
  • 14.3 Applying Panel Data Methods to Other Data Structures
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 15: Instrumental Variables Estimation and Two-Stage Least Squares

  • 15.1 Motivation: Omitted Variables and Measurement Error

  • 15.2 Instrumental Variables Estimation
  • 15.3 Two-Stage Least Squares (2SLS)
  • 15.4 The General IV Model
  • 15.5 Applying 2SLS to Time Series Equations
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 16: Simultaneous Equations Models

  • 16.1 The Nature of Simultaneous Equations Models
  • 16.2 Endogeneity and Reduced Forms

  • 16.3 Identification in a Two-Equation System
  • 16.4 Estimation in a Two-Equation System
  • 16.5 Systems with More Than Two Equations
  • 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
  • 17.2 The Tobit Model for Corner Solution Outcomes

  • 17.3 The Poisson Regression Model
  • 17.4 Censored and Truncated Regression Models
  • 17.5 Sample Selection Corrections
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 18: Advanced Time Series Topics

  • 18.1 Infinite Distributed Lag Models

  • 18.2 Cointegration and Error Correction Models
  • 18.3 Forecasting
  • Summary
  • Key Terms
  • Problems
  • Computer Exercises

Chapter 19: Carrying Out an Empirical Project

  • 19.1 Choosing a Topic
  • 19.2 Getting the Data
  • 19.3 Doing Your Econometric Analysis
  • 19.4 Writing Your Empirical Paper
  • Summary

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Tags: Jeffrey M Wooldridge, Introductory, Econometrics