Informationen zum Autor Genevieve Briand and R. Carter Hill are the authors of Using Excel for Principles of Econometrics, 4th Edition, published by Wiley. Klappentext Principles of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer. Inhaltsverzeichnis 1. Introduction to Excel 1 2. The Simple Linear Regression Model 19 3. Interval Estimation and Hypothesis Testing 67 4. Prediction, Goodness-of-Fit and Modeling Issues 95 5. The Multiple Linear Regression 143 6. Further Inference in the Multiple Regression Model 154 7. Using Indicator Variables 180 8. Heteroskedasticity 204 9. Regression with Time Series Data: Stationary Variables 228 10. Random Regressors and Moment-Based Estimation 262 11. Simultaneous Equations Models 278 12. Nonstationary Time-Series Data and Cointegration 294 13. Vector Error Correction and Vector Autoregressive Models 310 14. Time-Varying Volatility and ARCH Models 328 15. Panel Data Models 355 16. Qualitative and Limited Dependent Variable Models 391 A. Mathematical Tools 402 B. Review of Probability Concepts 416 C. Review of Statistical Inference 431 Index 466

Klappentext

Principles of Econometrics ****is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.

Inhalt

1. Introduction to Excel 1

2. The Simple Linear Regression Model 19

3. Interval Estimation and Hypothesis Testing 67

4. Prediction, Goodness-of-Fit and Modeling Issues 95

5. The Multiple Linear Regression 143

6. Further Inference in the Multiple Regression Model 154

7. Using Indicator Variables 180

8. Heteroskedasticity 204

9. Regression with Time Series Data: Stationary Variables 228

10. Random Regressors and Moment-Based Estimation 262

11. Simultaneous Equations Models 278

12. Nonstationary Time-Series Data and Cointegration 294

13. Vector Error Correction and Vector Autoregressive Models 310

14. Time-Varying Volatility and ARCH Models 328

15. Panel Data Models 355

16. Qualitative and Limited Dependent Variable Models 391

A. Mathematical Tools 402

B. Review of Probability Concepts 416

C. Review of Statistical Inference 431

Index 466