Richard Ashley is a professory of Economics at Virginia Tech. He earned his Ph.D in 1976 at the University of California, San Diego. Prior to VT, he taught economics at the University of Texas, Austin. His specialties and areas of interest include Econometrics and Macroeconomic Forecasting. He has received several teaching and research grants and has been published in Macroeconomic Dynamics, Journal of Applied Econometrics, Econometric Reviews, International Review of Economics and Finance, among others.

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• What’s Different about Thi' Book xiii Working with Data in the "Active Learning Exercises" xxiiAcknowledgments xxiiiNotation xxivPart I. Introduction and Statistics Review 1Chapter 1. Introduction 3Chapter 2. A Review of Probability Theory 11Chapter 3. Estimating the Mean of a Normally Distributed Random Variable 46Chapter 4. Statistical Inference on the Mean of a Normally Distributed Random Variable 68Part II. Regression Analysis 97Chapter 5. The Bivariate Regression Model: Introduction, Assumptions, and Parameter Estimates 99Chapter 6. The Bivariate Linear Regression Model: Sampling Distributions and Estimator Properties 131Chapter 7. The Bivariate Linear Regression Model: Inference on β 150Chapter 8. The Bivariate Regression Model: R2 and Prediction 178Chapter 9. The Multiple Regression Model 191Chapter 10. Diagnostically Checking and Respecifying the Multiple Regression Model: Dealing with Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case 224Chapter 11. Stochastic Regressors and Endogeneity 259Chapter 12. Instrumental Variables Estimation 303Chapter 13. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part A) 342Chapter 14. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part B) 389Part III. Additional Topics in Regression Analysis 455Chapter 15. Regression Modeling with Panel Data (Part A) 459Chapter 16. Regression Modeling with Panel Data (Part B) 507Chapter 17. A Concise Introduction to Time-Series Analysis and Forecasting (Part A) 536Chapter 18. A Concise Introduction to Time-Series Analysis and Forecasting (Part B) 595Chapter 19. Parameter Estimation Beyond Curve-Fitting: MLE (With an Application to Binary-Choice Models) and GMM (With an Application to IV Regression) 647Chapter 20. Concluding Comments 681Mathematics Review 693Index 699

Richard Ashley is a professory of Economics at Virginia Tech. He earned his Ph.D in 1976 at the University of California, San Diego. Prior to VT, he taught economics at the University of Texas, Austin. His specialties and areas of interest include Econometrics and Macroeconomic Forecasting. He has received several teaching and research grants and has been published in Macroeconomic Dynamics, Journal of Applied Econometrics, Econometric Reviews, International Review of Economics and Finance, among others.

Select Guide Rating

• What’s Different about Thi' Book xiii Working with Data in the "Active Learning Exercises" xxiiAcknowledgments xxiiiNotation xxivPart I. Introduction and Statistics Review 1Chapter 1. Introduction 3Chapter 2. A Review of Probability Theory 11Chapter 3. Estimating the Mean of a Normally Distributed Random Variable 46Chapter 4. Statistical Inference on the Mean of a Normally Distributed Random Variable 68Part II. Regression Analysis 97Chapter 5. The Bivariate Regression Model: Introduction, Assumptions, and Parameter Estimates 99Chapter 6. The Bivariate Linear Regression Model: Sampling Distributions and Estimator Properties 131Chapter 7. The Bivariate Linear Regression Model: Inference on β 150Chapter 8. The Bivariate Regression Model: R2 and Prediction 178Chapter 9. The Multiple Regression Model 191Chapter 10. Diagnostically Checking and Respecifying the Multiple Regression Model: Dealing with Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case 224Chapter 11. Stochastic Regressors and Endogeneity 259Chapter 12. Instrumental Variables Estimation 303Chapter 13. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part A) 342Chapter 14. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part B) 389Part III. Additional Topics in Regression Analysis 455Chapter 15. Regression Modeling with Panel Data (Part A) 459Chapter 16. Regression Modeling with Panel Data (Part B) 507Chapter 17. A Concise Introduction to Time-Series Analysis and Forecasting (Part A) 536Chapter 18. A Concise Introduction to Time-Series Analysis and Forecasting (Part B) 595Chapter 19. Parameter Estimation Beyond Curve-Fitting: MLE (With an Application to Binary-Choice Models) and GMM (With an Application to IV Regression) 647Chapter 20. Concluding Comments 681Mathematics Review 693Index 699

Richard Ashley is a professory of Economics at Virginia Tech. He earned his Ph.D in 1976 at the University of California, San Diego. Prior to VT, he taught economics at the University of Texas, Austin. His specialties and areas of interest include Econometrics and Macroeconomic Forecasting. He has received several teaching and research grants and has been published in Macroeconomic Dynamics, Journal of Applied Econometrics, Econometric Reviews, International Review of Economics and Finance, among others.

Select Guide Rating

• What’s Different about Thi' Book xiii Working with Data in the "Active Learning Exercises" xxiiAcknowledgments xxiiiNotation xxivPart I. Introduction and Statistics Review 1Chapter 1. Introduction 3Chapter 2. A Review of Probability Theory 11Chapter 3. Estimating the Mean of a Normally Distributed Random Variable 46Chapter 4. Statistical Inference on the Mean of a Normally Distributed Random Variable 68Part II. Regression Analysis 97Chapter 5. The Bivariate Regression Model: Introduction, Assumptions, and Parameter Estimates 99Chapter 6. The Bivariate Linear Regression Model: Sampling Distributions and Estimator Properties 131Chapter 7. The Bivariate Linear Regression Model: Inference on β 150Chapter 8. The Bivariate Regression Model: R2 and Prediction 178Chapter 9. The Multiple Regression Model 191Chapter 10. Diagnostically Checking and Respecifying the Multiple Regression Model: Dealing with Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case 224Chapter 11. Stochastic Regressors and Endogeneity 259Chapter 12. Instrumental Variables Estimation 303Chapter 13. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part A) 342Chapter 14. Diagnostically Checking and Respecifying the Multiple Regression Model: The Time-Series Data Case (Part B) 389Part III. Additional Topics in Regression Analysis 455Chapter 15. Regression Modeling with Panel Data (Part A) 459Chapter 16. Regression Modeling with Panel Data (Part B) 507Chapter 17. A Concise Introduction to Time-Series Analysis and Forecasting (Part A) 536Chapter 18. A Concise Introduction to Time-Series Analysis and Forecasting (Part B) 595Chapter 19. Parameter Estimation Beyond Curve-Fitting: MLE (With an Application to Binary-Choice Models) and GMM (With an Application to IV Regression) 647Chapter 20. Concluding Comments 681Mathematics Review 693Index 699