π 2023-10-25 β Session: Data Cleaning and Statistical Analysis Session
π 00:35β22:44
π·οΈ Labels: Python, Data Cleaning, Variance Analysis, Econometrics, Latex, Comovements
π Project: Dev
β Priority: MEDIUM
Session Goal: The session aimed to address data cleaning in Python and explore statistical analyses related to economic data and variance transformations.
Key Activities:
- Converted βagrupacion_idβ in a Pandas DataFrame to zero-filled strings, handling NaN values.
- Resolved a type conversion error in Python by adjusting the
harmonize_agrupacion_id
function. - Conducted an analysis of variance in firm-level shocks transformation, exploring variance decomposition and implications for log-normal and log-Laplace distributions.
- Extended covariance analysis for shock transformations, focusing on relationships between variances and covariances.
- Analyzed the nonlinear impact on variance through micro shocks and explored the influence of comovements on variance in aggregated measures.
- Developed a LaTeX template for econometric papers and revised captions for data visualization plots.
Achievements:
- Successfully cleaned and manipulated data in Python using Pandas.
- Clarified statistical concepts related to variance and covariance in economic and complex systems.
- Prepared a LaTeX template for future econometric research publications.
Pending Tasks:
- Further research on the implications of comovements in agent-based models and variance scaling.
- Explore more on the scaling exponentβs role in variance decay with population size.