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Solution Manual for Introduction to Econometrics 4th by Stock

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Product Details:

  • ISBN-10 ‏ : ‎ 0134461991
  • ISBN-13 ‏ : ‎ 978-0134461991
  • Author:   James Stock (Author), Mark Watson (Author)

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.

 

Table of Content:

PART I: INTRODUCTION AND REVIEW

  1. Economic Questions and Data
  2. Review of Probability
  3. Review of Statistics

PART II: FUNDAMENTALS OF REGRESSION ANALYSIS

  1. Linear Regression with One Regressor
  2. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
  3. Linear Regression with Multiple Regressors
  4. Hypothesis Tests and Confidence Intervals in Multiple Regression
  5. Nonlinear Regression Functions
  6. Assessing Studies Based on Multiple Regression

PART III: FURTHER TOPICS IN REGRESSION ANALYSIS

  1. Regression with Panel Data
  2. Regression with a Binary Dependent Variable
  3. Instrumental Variables Regression
  4. Experiments and Quasi-Experiments
  5. Prediction with Many Regressors and Big Data

PART IV: REGRESSION ANALYSIS OF ECONOMIC TIME SERIES DATA

  1. Introduction to Time Series Regression and Forecasting
  2. Estimation of Dynamic Causal Effects
  3. Additional Topics in Time Series Regression

PART V: THE ECONOMIC THEORY OF REGRESSION ANALYSIS

  1. The Theory of Linear Regression with One Regressor
  2. The Theory of Multiple Regression

 

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