Index

A

A Problem that Stumped Milton Friedman, [1]

An Introduction to Job Search Asset Pricing: Finite State Models, [1]

C

Central Limit Theorem, [1] Intuition Multivariate Case

CLT, [1]

D

Dynamic Programming Computation, [1] Theory Unbounded Utility

E

Eigenvalues, [1]

Eigenvectors, [1] Ergodicity, [1]

F

Finite Markov Asset Pricing Lucas Tree

Finite Markov Chains, [1], [2] Stochastic Matrices

I

Irreducibility and Aperiodicity, [1]

J

Job Search VI: On-the-Job Search, [1]

K

Kalman Filter Programming Implementation Recursive Procedure

Kalman Filter 2 Kesten processes heavy tails

L

Lake Model Law of Large Numbers, [1] Illustration Multivariate Case Proof Linear Algebra Differentiating Linear and Quadratic Forms Eigenvalues Eigenvectors Matrices Matrix Norms Neumann's Theorem Positive Definite Matrices SciPy Series Expansions Spectral Radius Vectors

Linear Markov Perfect Equilibria Linear State Space Models, [1] Distributions, [1] Ergodicity Martingale Difference Shocks Moments Moving Average Representations Prediction Seasonals Stationarity Time Trends Univariate Autoregressive Processes Vector Autoregressions LLN, [1] LQ Control Infinite Horizon Optimality (Finite Horizon)

M

Marginal Distributions, [1] Markov Asset Pricing Overview Markov Chains Calculating Stationary Distributions Convergence to Stationarity Cross-Sectional Distributions Ergodicity Forecasting Future Values Future Probabilities Irreducibility, Aperiodicity Marginal Distributions Simulation Stationary Distributions Markov Perfect Equilibrium Applications Background Overview Markov process, inventory

Matrix Determinants Inverse Maps Numpy Operations Solving Systems of Equations Modeling Career Choice Modeling COVID 19, [1] Models Harrison Kreps Linear State Space Markov Asset Pricing McCall On-the-Job Search Permanent Income, [1] Pricing Sequential analysis

N

Neumann's Theorem

O

On-the-Job Search Model Model Features Parameterization Programming Implementation Solving for Policies Optimal Growth Model, [1] Policy Function Policy Function Approach

Optimal Growth I: The Stochastic Optimal Growth Model, [1] Optimal Growth II: Accelerating the Code with Numba, [1] Optimal Growth III: Time Iteration, [1] Optimal Growth IV: The Endogenous Grid Method, [1] Optimal Savings Computation, [1] Problem Programming Implementation

P

Pandas for Panel Data, [1] Permanent Income II: LQ Techniques, [1] Permanent Income Model Hall's Representation Savings Problem Positive Definite Matrices

Pricing Models, [1] Risk Aversion Risk-Neutral Python Pandas python, [1]

R

Rational Expectations Equilibrium, [1] Competitive Equilbrium (w. Adjustment Costs) Computation Definition Planning Problem Approach

S

Spectral Radius Stability in Linear Rational Expectations Models

Stationary Distributions, [1] Stochastic Matrices

T

The Income Fluctuation Problem I: Basic Model, [1]

The Income Fluctuation Problem II: Stochastic Returns on Assets, [1] The Permanent Income Model, [1]

U

Unbounded Utility

V

Vectors, [1] Inner Product Linear Independence Norm Operations Span