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Kenneth L. Judd
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Kenneth L. Judd
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    • Dynamic Stochastic Integration of Climate and Economy
      • DICE_N vs DICE_CJL
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  • Teaching
    • 2009 Lectures at Peking and Renmin Universities
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    • Numerical Methods Course by Judd
      • Lecture 05: Nonlinear Equations
      • Lecture 06: Constrained optimization theory and methods
      • Lecture 07: Constrained optimization applications
      • Lecture 08: Structural Estimation I
      • Lecture 09: Finite-difference ODEs
      • Lecture 13: Approximation I
      • Lecture 21: Perturbation methods
      • Lecture 01: Introduction
      • Lecture 02: Computer Arithmetic
      • Lecture 03: Linear algebra and equations
      • Lecture 04: Unconstrained optimization
      • Lecture 10: Version Control Using Git
      • Lecture 11: Automatic Differentiation
      • Lecture 12: Homotopy
      • Lecture 14: Numerical quadrature MC qMC
      • Lecture 15: Dynamic optimization equilibrium NLCEQ
      • Lecture 16: Dynamic programming-discrete state
      • Lecture 17: Structural estimation II
      • Lecture 18: Dynamic programming-continuous state
      • Lecture 19: Projection methods I
      • Lecture 20: Projection Methods II
      • Lecture 22: Modern Approximation
      • Lecture 23: Dynamic Games
      • Lecture 24: Multi Objective Optimization
      • Lecture 25: Structural estimation III
      • Bonus Lecture: Macroeconomics
      • Lecture 26: DSICE
      • Lecture 27: Concluding remarks
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        • Penn State 2021, March 16: Discrete-state dynamic programming
        • Penn State 2021, March 18: MPEC, NFXP, Algorithms and Software
        • Penn State 2021, March 23: Not your grandfather’s confidence intervals
        • Penn State 2021, March 25: Macro-based empirical micro
        • Penn State 2021, March 25: SimEcon: A possible path for economics
        • Penn State 2021: Comments related to student presentations
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        • Approximation
        • Doraszelski-Judd Paper and RAND’s rejection
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        • Judd Maliar Maliar
        • Quadrature
        • Structural Estimation
      • Penn State 2012
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    • University of Zurich 2020
      • Bonus Lecture: Macroeconomics
      • Lecture 01: Introduction
      • Lecture 02: Computer Arithmetic
      • Lecture 03: Linear algebra and equations
      • Lecture 04: Unconstrained optimization
      • Lecture 05: Nonlinear Equations
      • Lecture 06: Constrained optimization theory and methods
      • Lecture 08: Structural Estimation I
      • Lecture 09: Finite-difference ODEs
      • Lecture 10: Version Control Using Git
      • Lecture 11: Automatic Differentiation
      • Lecture 12: Homotopy
      • Lecture 13: Approximation I
      • Lecture 14: Numerical quadrature MC qMC
      • Lecture 15: Dynamic optimization equilibrium NLCEQ
      • Lecture 16: Dynamic programming-discrete state
      • Lecture 17: Structural estimation II
      • Lecture 18: Dynamic programming-continuous state
      • Lecture 19: Projection methods I
      • Lecture 20: Projection Methods II
      • Lecture 21: Perturbation methods
      • Lecture 22: Modern Approximation
      • Lecture 23: Dynamic Games
      • Lecture 24: Multi Objective Optimization
      • Lecture 25: Structural estimation III
      • Lecture 26: DSICE
      • Lecture 27: Concluding remarks
      • Lecture 07: Constrained optimization applications
  • The Fed and its Models
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  • Trust No One
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Teaching

  • Numerical Methods Course by Judd
  • Penn State Numerical Methods
  • University of Zurich 2020 Numerical Methods in Economics
  • 2009 Lectures at Peking and Renmin Universities

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