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E470: Intro

E470: Intro. I. ECMT as Econ * Stats. Econ Theory  Model of Cause/Effect  Ecmt Model. I. ECMT as Econ * Stats. Econ Theory  Model of Cause/Effect  Ecmt Model Stat Facts  Identifiable Variables/Data  Refined Data. I. ECMT as Econ * Stats.

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E470: Intro

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  1. E470: Intro

  2. I. ECMT as Econ * Stats • Econ Theory  Model of Cause/Effect  Ecmt Model

  3. I. ECMT as Econ * Stats • Econ Theory  Model of Cause/Effect  Ecmt Model • Stat Facts  Identifiable Variables/Data  Refined Data

  4. I. ECMT as Econ * Stats • Econ Theory  Model of Cause/Effect  Ecmt Model • Stat Facts  Identifiable Variables/Data  Refined Data • Stat Theory  Ecmt Technique

  5. I. ECMT as Econ * Stats • Econ Theory  Model of Cause/Effect  Ecmt Model • Stat Facts  Identifiable Variables/Data  Refined Data • Stat Theory  Ecmt Technique  Stat Analysis, incl. Hypothesis Testing, Policy Evaluation, and Forecasting

  6. I. ECMT as Econ * Stats -moving from qualitative to quantitative

  7. I. ECMT as Econ * Stats -moving from qualitative to quantitative -positive vs. normative economics

  8. I. ECMT as Econ * Stats -moving from qualitative to quantitative -positive vs. normative economics -models and stats/data as proxies

  9. I. ECMT as Econ * Stats -moving from qualitative to quantitative -positive vs. normative economics -models and stats/data as proxies -role and importance of (good) theory

  10. II. Intro to regression analysis • sample equation: Y = B0 + (B1 * X) + E (error term) • when estimated: Y-hat = B0-hat + (B1-hat * X) + e (residual)

  11. II. Intro to regression analysis Y-hat = B0-hat + (B1-hat * X) + e (residual) • Dependent and Independent Variables (Y & X) -causality vs. correlation

  12. II. Intro to regression analysis Y-hat = B0-hat + (B1-hat * X) + e (residual) • Dependent and Independent Variables (Y & X) -causality vs. correlation • Estimated Coefficients (B0 and B1) -intercept (interpreting is tricky at best...) -slopes (linear, quadratic)

  13. II. Intro to regression analysis Y-hat = B0-hat + (B1-hat * X) + e (residual) • Dependent and Independent Variables (Y & X) -causality vs. correlation • Estimated Coefficients (B0 and B1) -intercept (interpreting is tricky at best...) -slopes (linear, quadratic) • Error Term (E); Residual (e) -goal: minimize residuals; find best fit -sources of residual: measurement error, mis-specified model --> points to import of good data, good theory --> points to the import of looking at and analyzing residuals

  14. II. Intro to regression analysis Y-hat = B0-hat + (B1-hat * X) + e (residual) • Error Term (E); Residual (e) -goal: minimize residuals; find best fit -sources of residual: measurement error, mis-specified model --> points to import of good data, good theory --> points to the import of looking at and analyzing residuals -observed Y has error term (vs. true line) vs. estimated/fitted value E(Y) or Y-hat (difference as residual) -residual = observed - estimated regression E(Y), a number -error term = observed - "true" regression E(Y), which cannot be measured

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