Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most fundamental econometric ideas and tools. The texts serves both the student whose interest is in understanding how one can use sample data to illuminate economic theory and the student who wants and needs a solid intellectual foundation on which to build practical experiential expertise. Starting with a unique Statistics review to start the book, students will learn by doing. Ashley provides students with integrated, hands-on exercises, and the text is supplemented with Active Learning Exercises.…mehr
Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most fundamental econometric ideas and tools. The texts serves both the student whose interest is in understanding how one can use sample data to illuminate economic theory and the student who wants and needs a solid intellectual foundation on which to build practical experiential expertise. Starting with a unique Statistics review to start the book, students will learn by doing. Ashley provides students with integrated, hands-on exercises, and the text is supplemented with Active Learning Exercises.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1. Introduction 2. A Review of Probability Theory 3. Estimating the Mean of a Normally Distributed Random Variable 4. Statistical Inference on the Mean of a Normally Distributed Random Variable 5. The Bivariate Regression Model: (Introduction, Assumptions, and Parameter Estimates) 6. The Bivariate Regression Model: (Sampling Distributions and Estimator Properties) 7. The Bivariate Regression Model: Inference on ß 8. The Bivariate Regression Model: R2 and Prediction 9. The Multiple Regression Model 10. Diagnostically Checking and Re-Specifying the Multiple Regression Model: Dealing With Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case 11. Stochastic Regressors and Endogeneity 12. Instrumental Variables Estimation 13. Diagnostically Checking and Re-Specifying the Multiple Regression 14. Diagnostically Checking and Re-Specifying the Multiple Regression 15. Regression Modeling with Panel Data (Part A) 16. Regression Modeling with Panel Data (Part B) 17. A Concise Introduction to Time-Series Analysis and Forecasting 18. A Concise Introduction to Time-Series Analysis and Forecasting 19. Parameter Estimation Beyond Curve-Fitting: MLE (with an Application to Binary-Choice Models) and GMM (with an Application to IV Regression) 20. Concluding Comments
1. Introduction 2. A Review of Probability Theory 3. Estimating the Mean of a Normally Distributed Random Variable 4. Statistical Inference on the Mean of a Normally Distributed Random Variable 5. The Bivariate Regression Model: (Introduction, Assumptions, and Parameter Estimates) 6. The Bivariate Regression Model: (Sampling Distributions and Estimator Properties) 7. The Bivariate Regression Model: Inference on ß 8. The Bivariate Regression Model: R2 and Prediction 9. The Multiple Regression Model 10. Diagnostically Checking and Re-Specifying the Multiple Regression Model: Dealing With Potential Outliers and Heteroscedasticity in the Cross-Sectional Data Case 11. Stochastic Regressors and Endogeneity 12. Instrumental Variables Estimation 13. Diagnostically Checking and Re-Specifying the Multiple Regression 14. Diagnostically Checking and Re-Specifying the Multiple Regression 15. Regression Modeling with Panel Data (Part A) 16. Regression Modeling with Panel Data (Part B) 17. A Concise Introduction to Time-Series Analysis and Forecasting 18. A Concise Introduction to Time-Series Analysis and Forecasting 19. Parameter Estimation Beyond Curve-Fitting: MLE (with an Application to Binary-Choice Models) and GMM (with an Application to IV Regression) 20. Concluding Comments
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