Implemented regression models for economic forecasting

• Day: 2024-09-28
• Time: 05:30 to 06:50
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Linear Regression, Economic Forecasting, Data Preparation, Python, Poverty Estimation

Description

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 [[pandas]] and scipy.
• 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.

Evidence

• source_file=2024-09-28.sessions.jsonl, line_number=1, event_count=0, session_id=667cefc6367d8ed9fe368ddc7e38e94e65fb948a3c19fae843cb0945f225f586
• event_ids: []