Microeconometrics: Methods and Applications A. Colin Cameron and Pravin K. Trivedi
MICROECONOMETRICS: Methods and Applications

Cambridge University Press, New York
May 2005

PART 1  (chapters 1-3)

Part 1 covers the essential components of microeconometric analysis -- an economic specification, a statistical  model and a data set.    

Chapter 1 discusses the distinctive aspects of microeconometrics, and provides an outline of the book. It emphasizes that discreteness of data, and nonlinearity and heterogeneity of behavioral relationships are key aspects of disaggregated microeconometric models. It concludes by presenting the notation and conventions used throughout the book. 

Chapters 2 and 3 set the scene for the remainder of the book by introducing the reader to key model and data concepts that shape the analyses of later chapters.

A key distinction in econometrics is between essentially descriptive models and data summaries at various levels of statistical sophistication and models that go beyond associations and attempt to estimate causal parameters. The classic definitions of causality in econometrics derive from the Cowles Commission simultaneous equations models that draw sharp distinctions between exogenous and endogenous variables, and between structure and reduced form parameters.  Although reduced form models are very useful for prediction, knowledge of structural or causal parameters is essential for policy analyses.  Identification of structural parameters within the simultaneous equations framework poses numerous conceptual and practical difficulties.  An alternative approach based on the potential outcome model, also attempts to identify causal parameters but it does so by posing limited questions within a more manageable framework. Chapter 2 attempts to provide an overview of the fundamental issues that arise in these alternative frameworks.  Readers who initially find this material challenging should return to this chapter later after gaining greater familiarity with specific models covered later in the book. 

The empirical researcher’s ability to identify causal parameters depends not only on the statistical tools and models but also on the type of data available. An experimental framework provides a standard for establishing causal connections. However, observational, not experimental, data form the basis of much of econometric inference. Chapter 3 surveys the pros and cons of three main types of data available: observational data, data from social experiments, and those from natural experiments. The potential as well as the difficulties of conducting causal inference based on each type of data are reviewed. 

Back to Main Page