Econ0019 Study guides, Revision notes & Summaries

A summary of Term 1 Quantitative Economics and Econometrics UCL 21/22 taught by Dennis Kristensen. Topics include: simple and multiple linear regression, sampling, large sample inference, further issues with MLR, qualitative information and dummy variables, heteroskedasticity, specification and data issues, and panel data methods. Final mark: 76%

A summary of Term 2 Quantitative Economics and Econometrics UCL 21/22 taught by Dennis Kristensen & Aureo de Paula. Topics include: potential outcomes, instrumental variables, binary response models, censoring, truncation, time series, and serial correlation. Final mark: 76%

Summary of Term 1 taught in ECON0019 (Year 2021/2022) Detailed notes from lecture notes, textbooks and other materials. Topics covered include: 1) Simple Linear Regression (SLR), 2) Multiple Linear Regression (MLR), 3) Finite Sample Inference in MLR, 4) Large-Sample Inference in MLR, 5) MLR with Qualitative Information, 6) Heteroskedasticity, 7) More on Specification and Data Issues, 8) Pooled Regressions and Panel Data Methods I, 9) Panel Data Methods II and 10) MLR: Further Issues.

Summary of Term 2 taught in ECON0019 (Year 2021/2022) Detailed notes from lecture notes, textbooks and other materials. Topics covered include: 1) Potential Outcomes and Experiments, 2) Instrumental Variables I, 3) Instrumental Variables II, 4) Simultaneous Equations Models, 5) Limited Dependent Variables I, 6) Limited Dependent Variables II, 7) Limited Dependent Variables III, 8) Limited Dependent Variables IV and Regression with Time Series I, 9) Regression with Time Series III and 10) S...

Adavanced economics STATA user guides for econometrics undergraduates. Includes all regressions and analysis covered Quantitative Economics and Econometrics (ECON019), Microeconometrics (ECON0021) and Econometrics for Macroeconomics and Finance (ECON0022).

• 49-pages detailed handwritten notes for ECON0019 (previously ECON2007) Quantitative Economics and Econometrics • Seller achieved a 1st for this module • Provide side-by-side comparisons for similar concepts to aid understanding and avoid confusion (eg. FD vs FE vs RE, Tobit vs Logit vs Probit, Random walk vs Random walk with drift, etc.) • Extracted all important formulae, assumptions, proofs from all lectures across both terms