Welcome to PyEconomics’s documentation!
PyEconomics is a Python library for economic and financial analysis, designed to provide tools and models for analyzing various aspects of economic, financial, and fiscal policy. Whether you’re a developer, economist, financial analyst, or researcher, PyEconomics aims to be your go-to resource for sophisticated economic modeling and analysis.
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Contents
Contents:
- Introduction
- Features
- Installation
- API Key
- Usage
- Example 1: Calculate Current Policy Rule Estimates
- Example 2: Adjust Taylor Rule for Effective Lower Bound (ELB) and Policy Inertia
- Example 3: Calculate Current Taylor Rule Estimates
- Example 4: Calculate and Plot Historical Policy Rule Estimates
- Example 5: Calculate and Plot the Adjusted Historical Policy Rules
- Docker
- Roadmap
- Contributing
- Contact
Introduction
PyEconomics is a versatile and comprehensive library that integrates various economic and financial models. It is designed to support the development and analysis of monetary policy rules, exchange rate models, macroeconomic and microeconomic models, discounted cash flow models, and much more.
Features
Current Features
Monetary Policy Rules: Implementation of well-known monetary policy rules.
Cache Management: Efficient caching for API calls to improve performance and reduce redundant data fetching.
Planned Features
Exchange Rate Models: Models for predicting future exchange rates.
Macroeconomic Models: Comprehensive macroeconomic models.
Microeconomic Models: Detailed microeconomic models.
Discounted Cash Flow Models: Advanced models for discounted cash flow analysis.
Financial Models: Including CAPM and portfolio optimization.
Fiscal Policy Models: Tools to analyze taxation, government spending, and public debt.
Additional Economic Models: Support for behavioral economics, agent-based models, and international trade models.
Installation
To install PyEconomics, use pip:
pip install pyeconomics
From Source
If you want to install the package from the source code, follow these steps:
Clone the repository:
git clone https://github.com/nathanramoscfa/pyeconomics.git
Change to the project directory:
cd pyeconomics
Install the package:
pip install .
API Key
Some features of PyEconomics require access to the FRED API. To use these features, you need to configure your FRED API access.
A detailed step-by-step guide on obtaining and securely storing your FRED API key is available here:
Usage
Here are some basic examples of how to use PyEconomics for calculating and visualizing monetary policy rules.
Example 1: Calculate Current Policy Rule Estimates
# Import pyeconomics
import pyeconomics as pyecon
# Calculate policy rule estimates
policy_estimates = pyecon.calculate_policy_rule_estimates(verbose=True)
Verbose Print Statement:
┌───────────────────────────────────────────────────────────────────────────────────┐
│ Interest Rate Policy Estimates │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Taylor Rule (TR) 6.17% │
│ Balanced Approach Rule (BAR) 6.68% │
│ Balanced Approach Shortfalls Rule (BASR) 5.66% │
│ First Difference Rule (FDR) 5.97% │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Federal Funds Rate (FFR) 5.50% │
├───────────────────────────────────────────────────────────────────────────────────┤
│ As of Date May 20, 2024 │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Policy Prescription │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Taylor Rule (TR) suggests raising the rate by 0.75%. │
│ Balanced Approach Rule (BAR) suggests raising the rate by 1.25%. │
│ Balanced Approach Shortfalls Rule (BASR) suggests raising the rate by 0.25%. │
│ First Difference Rule (FDR) suggests raising the rate by 0.50%. │
└───────────────────────────────────────────────────────────────────────────────────┘
Example 2: Adjust Taylor Rule for Effective Lower Bound (ELB) and Policy Inertia
# Import pyeconomics
import pyeconomics as pyecon
# Adjustment Parameters
rho = 0.7 # Policy Inertia Coefficient
apply_elb = True # Apply Effective Lower Bound
adjusted_policy_estimates = pyecon.calculate_policy_rule_estimates(
rho=rho,
apply_elb=apply_elb,
verbose=True
)
Verbose Print Statement:
┌───────────────────────────────────────────────────────────────────────────────────┐
│ Adjusted Interest Rate Policy Estimates │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Taylor Rule (TR) 5.70% │
│ Balanced Approach Rule (BAR) 5.86% │
│ Balanced Approach Shortfalls Rule (BASR) 5.55% │
│ First Difference Rule (FDR) 5.64% │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Federal Funds Rate (FFR) 5.50% │
├───────────────────────────────────────────────────────────────────────────────────┤
│ As of Date May 21, 2024 │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Adjusted Policy Prescription │
├───────────────────────────────────────────────────────────────────────────────────┤
│ Taylor Rule (TR) suggests raising the rate by 0.25%. │
│ Balanced Approach Rule (BAR) suggests raising the rate by 0.25%. │
│ Balanced Approach Shortfalls Rule (BASR) suggests maintaining the current rate. │
│ First Difference Rule (FDR) suggests raising the rate by 0.25%. │
└───────────────────────────────────────────────────────────────────────────────────┘
Example 3: Calculate Current Taylor Rule Estimates
# Import pyeconomics modules
import pyeconomics as pyecon
# Calculate policy rule estimates
policy_estimates = pyecon.taylor_rule(verbose=True)
Verbose Print Statement:
==== Economic Indicators =================================================
Current Inflation: 3.04%
Target Inflation: 2.00%
Current Unemployment Rate: 3.90%
Natural Unemployment Rate: 4.41%
Long-Term Real Interest Rate: 2.10%
Current Fed Rate: 5.50%
As of Date: May 21, 2024
==== Gaps ================================================================
Inflation Gap: 1.04%
Unemployment Gap: 0.51%
==== Taylor Rule =========================================================
Long-Term Real Interest Rate: 2.10%
Current Inflation: + 3.04%
Alpha * Inflation Gap: + 0.50 * 1.04%
Beta * Okun Factor * Unemployment Gap: + 0.50 * 2.00 * 0.51%
--------------------------------------------------------------------------
Unadjusted Taylor Rule Estimate: 6.17%
==== Adjusted Taylor Rule ================================================
Effective Lower Bound (ELB) Adjustment:
Maximum of Taylor Rule or ELB: max(6.17%, 0.12%)
--------------------------------------------------------------------------
Taylor Rule Adjusted for ELB: 6.17%
Policy Inertia Adjustment:
Policy Inertia Coefficient (rho): 0.70
Current Fed Rate: * 5.50%
Adjustment Coefficient (1 - rho): + (1 - 0.70)
Taylor Rule Adjusted for ELB: * 6.17%
--------------------------------------------------------------------------
Adjusted Taylor Rule Estimate: 5.70%
==== Policy Prescription =================================================
The Adjusted Taylor Rule Estimate is 0.20% higher than the Current
Fed Rate. The Fed should consider raising the interest rate by 0.25%.
Example 4: Calculate and Plot Historical Policy Rule Estimates
# Import pyeconomics modules
import pyeconomics as pyecon
# Calculate historical policy rates
historical_policy_estimates = pyecon.calculate_historical_policy_rates().dropna()
# Plot historical policy rates
pyecon.plot_historical_rule_estimates(historical_policy_estimates)
Example 5: Calculate and Plot the Adjusted Historical Policy Rules
# Import pyeconomics modules
import pyeconomics as pyecon
# Adjustment Parameters
rho = 0.7 # Policy Inertia Coefficient
apply_elb = True # Apply Effective Lower Bound
# Calculate adjusted historical policy rates
adjusted_historical_policy_estimates = pyecon.calculate_historical_policy_rates(
rho=rho,
apply_elb=apply_elb
).dropna()
# Plot adjusted historical policy rates
pyecon.plot_historical_rule_estimates(
adjusted_historical_policy_estimates,
adjusted=True
)
Examples
For more comprehensive examples, refer to the examples directory in the repository.
Roadmap
The following models and categories are planned for future development. Contributions are welcome!
Monetary Policy Models
Taylor Rule
Balanced Approach Rule
First Difference Rule
McCallum Rule
Orphanides Rule
Friedman Rule
Interest Rate Smoothing Models
Quantity of Money Theory
Fisher Equation
Cambridge Equation
Velocity of Money Models
Quantity Theory of Money (QTM)
Money Demand Function (Md)
Economic Models to Predict Future Exchange Rates
Purchasing Power Parity (PPP)
Interest Rate Parity (IRP)
Monetary Models of Exchange Rates
Portfolio Balance Models
Behavioral Equilibrium Exchange Rate (BEER)
Fundamental Equilibrium Exchange Rate (FEER)
Macroeconomic Models
IS-LM Model
AD-AS Model
Solow Growth Model
New Keynesian Models
RBC (Real Business Cycle) Model
DSGE (Dynamic Stochastic General Equilibrium) Models
Microeconomic Models
Supply and Demand Models
Consumer Choice Theory
Production Theory
Cost Functions
Game Theory Models
Market Structure Models (Perfect Competition, Monopoly, Oligopoly)
Discounted Cash Flow Models
Dividend Discount Model (DDM)
Free Cash Flow to Equity (FCFE)
Free Cash Flow to Firm (FCFF)
Net Present Value (NPV)
Internal Rate of Return (IRR)
Adjusted Present Value (APV)
Financial Models
Capital Asset Pricing Model (CAPM)
Arbitrage Pricing Theory (APT)
Black-Scholes Option Pricing Model
Bond Valuation Models
Credit Risk Models (e.g., Merton Model)
Portfolio Optimization Models (e.g., Markowitz Model)
Fiscal Policy Models
Taxation and Government Spending Models
Budget Deficit Models
Public Debt Models
Fiscal Multiplier Models
Laffer Curve
Ricardian Equivalence
Other Well-Known Economic Models
Behavioral Economics Models
Agent-Based Models
Environmental Economics Models
International Trade Models (e.g., Heckscher-Ohlin Model)
Labor Market Models
Health Economics Models
Contributing
Contributions are welcome! If you have a model you’d like to implement or an improvement to an existing model, please refer to our detailed Contributing Guide.
By participating in this project, you agree to abide by the Code of Conduct.
Thank you for your contributions!
Contact
If you have any questions, suggestions, or need support, feel free to reach out.
Nathan Ramos, CFA
Welcome to my GitHub! I’m Nathan, an independent Fintech Developer with a Chartered Financial Analyst (CFA) designation, specializing in Python-based financial analysis, quantitative strategies, and automated trading programs. Currently, I’m a fintech consultant open to new clients. Schedule a meeting with me to discuss collaborating on your next project or to learn more about my services.
Connect with Me
Twitter: @nathanramoscfa
Feel free to connect with me for any inquiries or collaboration opportunities.