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Topics in Advanced Econometrics
Business, Management & Finance Program
Madrid, Spain
Dates: 8/26/24 - 12/21/24
Topics in Advanced Econometrics
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Economics
Instruction in: English
Course Code: 14119
Transcript Source: Partner Institution
Course Details: Level 300, 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: This course is designed for students with a strong background in econometrics and statistics acquired in previous courses: Mathematics for Economics I and II, Statistics I and II, Econometrics, Econometric Techniques and Quantitative Economics.
DESCRIPTION
1. Finite sample properties of ordinary least squares (OLS): The classical regression model. The algebra of least squares. Finite sample properties of OLS. Hypothesis testing under normality. Relation to maximum likelihood. Generalized least squares.
2. Large sample theory: Review of limit theorems for sequences of random variables. Fundamental concepts in time-series analysis. Large-sample distribution of the OLS estimator. Hypothesis testing. Consistent estimation of the asymptotic variance of OLS estimators. Implications of conditional homoscedasticity. Testing conditional homoscedasticity. Least squares projection. Consistent estimates of projection coefficients. Testing for lack of autocorrelation.
3. Single-equation generalized method of moments (GMM): Endogeneity bias. The general formulation. Generalized method of moments defined. Large sample properties of GMM. Testing overidentified restrictions. Hypothesis testing by likelihood-ratio principle. Implications of conditional homoscedasticity.
4. Multiple-equations GMM: The multiple-equations model. Multiple-equation GMM defined. Large sample theory. Single-equation versus multiple-equations estimation. Special cases of multiple equations GMM: FIVE, 3SLS and SUR. Common coefficients.
2. Large sample theory: Review of limit theorems for sequences of random variables. Fundamental concepts in time-series analysis. Large-sample distribution of the OLS estimator. Hypothesis testing. Consistent estimation of the asymptotic variance of OLS estimators. Implications of conditional homoscedasticity. Testing conditional homoscedasticity. Least squares projection. Consistent estimates of projection coefficients. Testing for lack of autocorrelation.
3. Single-equation generalized method of moments (GMM): Endogeneity bias. The general formulation. Generalized method of moments defined. Large sample properties of GMM. Testing overidentified restrictions. Hypothesis testing by likelihood-ratio principle. Implications of conditional homoscedasticity.
4. Multiple-equations GMM: The multiple-equations model. Multiple-equation GMM defined. Large sample theory. Single-equation versus multiple-equations estimation. Special cases of multiple equations GMM: FIVE, 3SLS and SUR. Common coefficients.