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Quantitative Economics with Python

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  • Thomas J. Sargent
  • John Stachurski
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  • Table of Contents »
  • Undergraduate Course

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Undergraduate Course¶

  • All
  • Undergraduate course
  • Graduate course
  • About these Lectures
  • Introduction to Python
    • About Python
    • Setting up Your Python Environment
    • An Introductory Example
    • Functions
    • Python Essentials
    • OOP I: Introduction to Object Oriented Programming
    • OOP II: Building Classes
  • The Scientific Libraries
    • Python for Scientific Computing
    • NumPy
    • Matplotlib
    • SciPy
    • Numba
    • Parallelization
    • Pandas
  • Advanced Python Programming
    • Writing Good Code
    • More Language Features
    • Debugging
  • Data and Empirics
    • Pandas for Panel Data
    • Linear Regression in Python
    • Maximum Likelihood Estimation
  • Tools and Techniques
    • Geometric Series for Elementary Economics
    • Linear Algebra
    • Complex Numbers and Trigonometry
    • LLN and CLT
    • Linear State Space Models
    • Finite Markov Chains
    • A First Look at the Kalman Filter
  • Dynamic Programming
    • Shortest Paths
    • Job Search I: The McCall Search Model
    • Job Search II: Search and Separation
    • Optimal Savings I: The Permanent Income Model
    • Optimal Savings II: LQ Techniques
    • Consumption Smoothing with Complete and Incomplete Markets
  • LQ Control
    • LQ Control: Foundations
    • Optimal Savings I: The Permanent Income Model
  • Multiple Agent Models
    • Schelling’s Segregation Model
    • A Lake Model of Employment and Unemployment
    • Rational Expectations Equilibrium
    • Stability in Linear Rational Expectations Models
    • Markov Perfect Equilibrium
  • Asset Pricing and Finance
    • Asset Pricing I: Finite State Models
  • References

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