Index

A

AR	ARMA, [1] ARMA Processes

B

Bellman Equation

C

Complex Numbers Continuous State Markov Chains Covariance Stationary

Covariance Stationary Processes AR MA

D

Discrete State Dynamic Programming

E

Eigenvalues

Eigenvectors Ergodicity

F

Finite Markov Chains, [1] Stochastic Matrices

G

General Linear Processes

I

Irreducibility and Aperiodicity

L

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

M

MA Marginal Distributions Markov Chains Calculating Stationary Distributions Continuous State Convergence to Stationarity Cross-Sectional Distributions Ergodicity Forecasting Future Values Future Probabilities Irreducibility, Aperiodicity Marginal Distributions Simulation Stationary Distributions

Markov Perfect Equilibrium Applications Overview Matrix Determinants Inverse Maps Numpy Operations Solving Systems of Equations Models Additive functionals

N

Neumann's Theorem

Nonparametric Estimation

P

Periodograms Computation Interpretation

Positive Definite Matrices python, [1]

R

Robustness

S

Smoothing Spectra Estimation Spectra, Estimation AR(1) Setting Fast Fourier Transform Pre-Filtering Smoothing, [1], [2]

Spectral Analysis, [1] Spectral Densities Spectral Density interpretation Inverting the Transformation Mathematical Theory Spectral Radius Stationary Distributions Stochastic Matrices

V

Vectors Inner Product Linear Independence Norm Operations Span

W

White Noise, [1]

Wold Representation