Semiparametric Regression for the Social Sciences [Hardcover]

An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Each chapter includes exercises and examples and there is a supplementary website containing all the datasets used, as well as computer code, allowing readers to replicate every analysis reported in the book. Includes software for implementing the methods in S-Plus and R.List of Tables.

List of Figures.

Preface.

1 Introduction: Global versus Local Statistics.

1.1 The Consequences of Ignoring Nonlinearity.

1.2 Power Transformations.

1.3 Nonparametric and Semiparametric Techniques.

1.4 Outline of the Text.

2 Smoothing and Local Regression.

2.1 Simple Smoothing.

2.1.1 Local Averaging.

2.1.2 Kernel Smoothing.

2.2 Local Polynomial Regression.

2.3 Nonparametric Modeling Choices.

2.3.1 The Span.

2.3.2 Polynomial Degree and Weight Function.

2.3.3 A Note on Interpretation.

2.4 Statistical Inference for Local Polynomial Regression.

2.5 Multiple Nonparametric Regression.

2.6 Conclusion.

2.7 Exercises.

3 Splines.

3.1 Simple Regression Splines.

3.1.1 Basis Functions.

3.2 Other Spline Models and Bases.

3.2.1 Quadratic and Cubic Spline Bases.

3.2.2 Natural Splines.

3.2.3 B-splines.

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