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Ch. 2 Tools of Positive Economics

Ch. 2 Tools of Positive Economics. Theoretical Tools of Public Finance. theoretical tools The set of tools designed to understand the mechanics behind economic decision making. empirical tools The set of tools designed to analyze data and answer questions raised by theoretical analysis.

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Ch. 2 Tools of Positive Economics

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  1. Ch. 2 Tools of Positive Economics

  2. Theoretical Tools of Public Finance • theoretical tools The set of • tools designed to understand • the mechanics behind economic decision making. • empirical tools The set of tools designed to analyze data and answer questions raised by theoretical analysis.

  3. The Role of Theory • Economic models • virtue of simplicity • judging a model • limitations of models • Empirical analysis

  4. There are many examples where causation and correlation can get confused. • In statistics, this is called the identification problem: given that two series are correlated, how do you identify whether one series is causing another?

  5. Causation vs. Correlation • Statistical analysis • Correlation • Control group • Treatment group • Conditions required for government action X to cause societal effect Y • X must precede Y • X and Y must be correlated • Other explanations for any observed correlation must be eliminated

  6. Experimental Studies • Biased estimates • Counterfactual • Experimental (or randomized) study

  7. Conducting an Experimental Study • Random assignment to control and treatment groups

  8. Pitfalls of Experimental Studies • Ethical issues • Technical problems • Response bias • Impact of limited duration of experiment • Generalization of results to other populations, settings, and related treatments • Black box aspect of experiments

  9. Observational Studies • Observational study – empirical study relying on observed data not obtained from experimental study • Sources of observational data • Surveys • Administrative records • Governmental data • Econometrics • Regression analysis (American Wind Energy Association, 2007)

  10. L = α0 + α1wn + α2X1 + … + αnXn + ε Dependent variable Independent variables Parameters Stochastic error term Regression analysis Regression line Standard error L wn Conducting an Observational Study Slopeis α1 Interceptis α0 α0

  11. Types of Data • Cross-sectional data • Time-series data • Panel data

  12. 3 . 3 • Time Series Analysis • Estimating Causation with Data We Actually Get: Observational Data

  13. 3 . 3 • Time Series Analysis • Estimating Causation with Data We Actually Get: Observational Data • When Is Time Series Analysis Useful?

  14. 3 . 3 • Cross-Sectional Regression Analysis • Estimating Causation with Data We Actually Get: Observational Data • Example with Real-World Data

  15. Pitfalls of Observational Studies • Data collected in non-experimental setting • Specification issues

  16. Quasi-Experimental Studies • Quasi-experimental study (= natural experiment) – observational study relying on circumstances outside researcher’s control to mimic random assignment

  17. 3 . 3 • Quasi-Experiments • Estimating Causation with Data We Actually Get: Observational Data

  18. Conducting a Quasi-Experimental Study • Difference-in-difference quasi-experiments • Instrumental Variables quasi-experiments • Regression-Discontinuity Quasi-Experiments

  19. Pitfalls of Quasi-Experimental Studies • Assignment to control and treatment groups may not be random • Not applicable to all research questions • Generalization of results to other settings and treatments

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