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Full Description
The second edition of this bestselling textbook retains its unique learning-by-doing approach to econometrics. Rather than relying on complex theoretical discussions and complicated mathematics, this book explains econometrics from a practical point of view by walking the student through real-life examples, step by step. Damodar Gujarati's clear, concise, writing style guides students from model formulation, to estimation and hypothesis-testing, through to post-estimation diagnostics. The basic statistics needed to follow the book are covered in an appendix, making the book a flexible and self-contained learning resource.
The textbook is ideal for undergraduate students in economics, business, marketing, finance, operations research and related disciplines. It is also intended for students in MBA programs across the social sciences, and for researchers in business, government and research organizations who require econometrics.
New to this Edition:
- Two brand new chapters on Quantile Regression Modeling and Multivariate Regression Models.
- Two further additional chapters on hierarchical linear regression models and bootstrapping are available on the book's website
- New extended examples accompanied by real-life data
- New student exercises at the end of each chapter
Accompanying online resources for this title can be found at bloomsburyonlineresources.com/econometrics-by-example-2. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost.
Contents
PART I: BASICS OF LINEAR REGRESSION
1. The Linear Regression Model
2. Functional Forms of Regression Models
3. Qualitative Explanatory Variables Regression Models
PART II: REGRESSION DIAGNOSTICS
4. Regression Diagnostic I: Multicollinearity
5. Regression Diagnostic II: Heteroscedasticity
6. Regression Diagnostic III: Autocorrelation
7. Regression Diagnostic IV: Model Specification Errors
PART III: REGRESSION MODELS WITH CROSS
SECTIONAL DATA
8. Stochastic Regressors and the Method of Instrumental Variables
9. The Logit and Probit Models
10. Multinomial Regression Models
11. Ordinal Regression Models
12. Limited Dependent Variable Regression Models
PART IV: TIME SERIES ECONOMETRICS
13. Modeling Count Data
14. Stationary and Nonstationary Time Series
15. Conintegration and Error Correction Models
16. Asset Price Volatility: the ARCH and GARCH Models
PART V: SELECTED TOPICS IN ECONOMETRICS
17. Economic Forecasting
18. Panel Data Regression Models
19. Stochastic Regressors and the Method of Instrumental Variables
20. Quantile Regression Modeling
21. Multivariate Regression Models.
