📅 2024-09-28 — Session: Implemented regression models for economic forecasting
🕒 05:30–06:50
🏷️ Labels: Linear Regression, Economic Forecasting, Data Preparation, Python, Poverty Estimation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to implement and refine regression models for economic forecasting, focusing on predicting poverty levels and extrapolating economic indicators.
Key Activities
- Linear Extrapolation: Implemented linear regression models to forecast Canasta Básica Alimentaria, IPC, RIPTE, and labor statistics using Python. This involved data loading, model fitting, extrapolation, and visualization.
- Poverty Prediction Models: Explored and planned regression models to predict poverty levels using various economic indicators and employment metrics. Detailed the model structure, variables, and validation steps.
- Data Preparation: Prepared time series datasets for regression modeling by combining economic indicators, handling missing data, and ensuring proper alignment.
- Interpolation: Applied quadratic spline interpolation to transform quarterly data into monthly frequency using
pandasandscipy. - Handling Missing Values: Developed methods to handle missing values in regression models using masked arrays and data filtering techniques in Python.
Achievements
- Successfully implemented linear regression models for economic forecasting.
- Established a framework for poverty prediction using regression analysis.
- Enhanced data preparation techniques for regression modeling.
Pending Tasks
- Validate and refine the poverty prediction models with additional data and diagnostics.
- Explore alternative interpolation methods for improved accuracy.