• 522 Reading List

522 Reading List : Last Revised 02/14/07

1. Statistical Inference (continuation of 520)

1. Hypothesis Testing (CB 8.1, 8.3.1-8.3.2)
2. Most Powerful Tests (CB 8.3.2-8.3.3)
3. Large Sample Tests (CB 10.3)
4. Confidence Intervals (CB 9.1-9.2, 10.4)

2. Regression Modeling

1. Introduction: Modeling multivariate relationships (Ruud 1)
2. Multivariate and Conditional Normal Model
   1. MVN distribution: Ruud 10.5.1
   2. MLE for MVN model: notes
   3. MLE for conditional normal model: notes
3. Conditional Normal Regression Model
   1. MLE and OLS (notes)
   2. Properties of OLS estimator (notes, Ruud 10)
   3. Testing and Confidence Intervals (Ruud 10, 11)
4. Neoclassical Linear Regression Model: Finite Sample Properties
   1. Model assumptions (Ruud 6.1-6.2, 7.1-7.4)
   2. Unbiasedness (Ruud 6.2)
   3. Variance/covariance (Ruud 8)
   4. Efficiency: Gauss-Markov theorem (Ruud 9)
5. Neoclassical Linear Regression Model: Large Sample Properties
   1. Consistency and Asymptotic Normality (notes, Ruud 13.4)
   2. Large-sample inference (notes, Ruud 13.4)
6. Extending the Neoclassical Regression Model:
   1. Heteroskedasticity (notes, Ruud 18)
   2. Serial Correlation (Ruud 19)
   3. OLS as a moment estimator (notes)

3. Causal Modeling

1. Omitted Variables Formula (notes)
2. Interlude: from correlation to causation (notes)
3. Linear instrumental variables (Ruud 20)
4. Simultaneous equations models (Ruud 26)
5. Introduction to GMM estimation (Ruud 21,22)