Tax Receipts Forecasting Dashboard

Overview

The Tax Receipts Forecasting Dashboard is an interactive tool designed to model and forecast tax revenue under various economic scenarios. Built with Streamlit, this dashboard uses SARIMAX models with exogenous variables to generate insights into the impact of economic factors like Corporate Profits (CP) and Job Openings (JTSJOL) on tax receipts.

See the app here: https://chrissimmerman-tax-receipt-forecasting-streamlit-app-6ftqol.streamlit.app

Key Features:

• 📊 Dynamic Scenario Modeling: Simulate scenarios such as recessions, booms, policy interventions, and pandemics.
• 🔮 Extrapolative Forecasting: Predict tax receipts and key economic indicators over a user-defined time horizon.
• 📈 Customizable Visualizations: Explore trends in Corporate Profits, Job Openings, and Tax Receipts using intuitive plots.
• ⚙️ Machine Learning Models: Leverage SARIMAX and Unobserved Components Models (UCM) for accurate predictions.

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Table of Contents

• Overview
• Features
• Installation
• Usage
• Technical Details
• License

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Features

• Scenario Analysis:

  • Recession: Gradual decline in economic activity.
  • Boom: Economic growth with increasing profits and job openings.
  • Policy Intervention: Short-term boost due to fiscal or monetary policies.
  • Pandemic: Sharp declines followed by recovery trends.

• Forecast Customization:

  • Adjustable forecast horizons (6-60 months).
  • Confidence intervals for forecast uncertainty.

• Data Insights:

  • Real-time plotting of historical and forecasted data.

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Installation

Prerequisites:

• Python 3.8+
• Virtual environment (optional but recommended)

Steps:

1. Clone the repository:

   git clone https://github.com/chrissimmerman/tax-receipts-forecast.git
   cd tax-receipts-forecast

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run streamlit_app.py

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Usage

1. Launch the app in your browser by following the instructions in the terminal.
2. Use the sidebar to:
   • Select a scenario to model.
   • Adjust the forecast horizon.
   • Toggle confidence intervals.
3. Explore the forecasted trends in tax receipts, Corporate Profits (CP), and Job Openings (JTSJOL).

Technical Details

Machine Learning Models:

• SARIMAX:
  • Predicts tax receipts with exogenous variables (CP, JTSJOL).
• UCM (Unobserved Components Model):
  • Models trends in Corporate Profits (CP).

Data Sources:

• FRED API:
  • Corporate Profits (CP)
  • Job Openings (JTSJOL)
  • Consumer Price Index (CPIAUCSL)
• US Treasury Deptartment API:
  • Monthly Treasury Statement (MTS)
• Custom transformations for inflation-adjusted data.
• Linear Interpolation of Corporate Profits (CP)