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Econometrics and Data Analysis for Developing Countries

Authors:
Chandan Mukherjee, Howard White, and Marc Wuyts
Publisher: Routledge
Copyright: 1998
ISBN-13: 978-0-415-09400-9
Pages: 496; paperback

New edition forthcoming

Comment from the Stata technical group

Chandan Mukherjee, Howard White, and Marc Wuyts provide an interesting introduction to econometrics in Econometrics and Data Analysis for Developing Countries.

The authors cover an impressive range of topics, emphasizing the importance of model specification and evaluating different methods for obtaining acceptable specifications. They also discuss several graphical methods that are frequently overlooked in treatments of econometrics and point out that graphical methods can be informative even without p-values.

This book does not discuss methods that are robust to distributional assumptions and does not make it clear that only small-sample statistics require the normality assumption of linear regression to be valid. (Asymptotically, the standard errors are consistent, and the test statistics converge to standard distributions without assuming that the disturbances are normally distributed.) If you are interested in a more formal treatment of this topic with emphasis on robust methods, you might want to supplement this book with Introductory Econometrics: A Modern Approach (Wooldridge 1999).

What this book does well is provide practical approaches to data analysis and model development, with an emphasis on development economics. It will be a valuable tool for those who need to analyze policy based on quantitative information.


Table of contents

List of figures
List of tables
List of boxes
Preface
Introduction
1  The purpose of this book
2  The approach of this book: an example
Part I     Foundations of data analysis
1 Model specification and applied research
1.1  Introduction
1.2  Model specification and statistical inference
1.3  The role of data in model specification: traditional modelling
1.4  The role of data in model specification: modern approaches
1.5  The time dimension in data
1.6  Summary of main points
2 Modelling an average
2.1  Introduction
2.2  Kinds of averages
2.3  The assumptions of the model
2.4  The sample mean as best linear unbiased estimator (BLUE)
2.5  Normality and the maximum likelihood principle
2.6  Inference from a sample of a normal distribution
2.7  Summary of main points
Appendix 2.1: Properties of mean and variance
Appendix 2.2: Standard sampling distributions
3 Outliers, skewness and data transformations
3.1  Introduction
3.2  The least squares principle and the concept of resistance
3.3  Mean-based versus order-based sample statistics
3.4  Detecting non-normality in data
3.5  Data transformations to eliminate skewness
3.6  Summary of main points
Part II     Regression and data analysis
4 Data analysis and simple regression
4.1  Introduction
4.2  Modelling simple regression
4.3  Linear regression and the least squares principle
4.4  Inference from classical normal linear regression model
4.5  Regression with graphics: checking the model assumptions
4.6  Regression through the origin
4.7  Outliers, leverage and influence
4.8  Transformation towards linearity
4.9  Summary of main points
5 Partial regression: interpreting multiple regression coefficients
5.1  Introduction
5.2  The price of food and the demand for manufactured goods in India
5.3  Least squares and the sample multiple regression line
5.4  Partial regression and partial correlation
5.5  The linear regression model
5.6  The t-test in multiple regression
5.7  Fragility analysis: making sense of regression coefficients
5.8  Summary of main points
6 Model selection and misspecification in multiple regression
6.1  Introduction
6.2  Griffin's aid versus savings model: the omitted variable bias
6.3  Omitted variable bias: the theory
6.4  Testing zero restrictions
6.5  Testing non-zero linear restrictions
6.6  Tests of parameter stability
6.7  The use of dummy variables
6.8  Summary of main points
Part III     Analysing cross-section data
7 Dealing with heteroscedasticity
7.1  Introduction
7.2  Diagnostic plots: looking for heteroscedasticity
7.3  Testing for heteroscedasticity
7.4  Transformations towards homoscedasticity
7.5  Dealing with genuine heteroscedasticity: weighted least squares and heteroscedastic standard errors
7.6  Summary of main points
8 Categories, counts and measurements
8.1  Introduction
8.2  Regression on a categorical variable: using dummy variables
8.3  Contingency tables: association between categorical variables
8.4  Partial association and interaction
8.5  Multiple regression on categorical variables
8.6  Summary of main points
9 Logit transformation, modelling and regression
9.1  Introduction
9.2  The logit transformation
9.3  Logit modelling with contingency tables
9.4  The linear probability model versus logit regression
9.5  Estimation and hypothesis testing in logit regression
9.6  Graphics and residual analysis in logit regression
9.7  Summary of main points
Part IV     Regression with time-series data
10 Trends, spurious regressions and transformations to stationarity
10.1  Introduction
10.2  Stationarity and non-stationarity
10.3  Random walks and spurious regression
10.4  Testing for stationarity
10.5  Transformations to stationarity
10.6  Summary of main points
Appendix 10.1: Generated DSP and TSP series for exercises
11 Misspecification and autocorrelation
11.1  Introduction
11.2  What is autocorrelation and why is it a problem?
11.3  Why do we get autocorrelation?
11.4  Detecting autocorrelation
11.5  What to do about autocorrelation
11.6  Summary of main points
Appendix 11.1: Derivation of variance and covariance for AR(1) model
12 Cointegration and the error correction model
12.1  Introduction
12.2  What is cointegration?
12.3  Testing for cointegration
12.4  The error correction model (ECM)
12.5  Summary of main points
Part V     Simultaneous equation models
13 Misspecification bias from single equation estimation
13.1  Introduction
13.2  Simultaneity bias in a supply and demand model
13.3  Simultaneity bias: the theory
13.4  The Granger and Sims tests for causality and concepts of exogeneity
13.5  The identification problem
13.6  Summary of main points
14 Estimating simultaneous equation models
14.1  Introduction
14.2  Recursive models
14.3  Indirect least squares
14.4  Instrumental variable estimation and two-stage least squares
14.5  Estimating the consumption function in a simultaneous system
14.6  Full information estimation techniques
14.7  Summary of main points
Appendix A: The data sets used in this book
Appendix B: Statistical tables
References
Index
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