Computational Economics

5.

Simulation Methods

5.1.

Random Number Generation

5.1.1.

Pseudorandom Number Generators

5.1.1.1.

Linear Congruential Generators

5.1.1.2.

Mersenne Twister

5.1.1.3.

Quality Assessment

5.1.2.

Random Sampling Techniques

5.1.2.1.

Inverse Transform Method

5.1.2.2.

Acceptance-Rejection Method

5.1.2.3.

Box-Muller Transform

5.1.3.

Seed Management

5.1.3.1.

Reproducibility Issues

5.1.3.2.

5.2.

Monte Carlo Methods

5.2.1.

Basic Monte Carlo Simulation

5.2.1.1.

Law of Large Numbers

5.2.1.2.

Central Limit Theorem

5.2.1.3.

Convergence Diagnostics

5.2.2.

Variance Reduction

5.2.2.1.

Antithetic Variates

5.2.2.2.

Control Variates

5.2.2.3.

Importance Sampling

5.2.2.4.

Stratified Sampling

5.2.3.

Markov Chain Monte Carlo

5.2.3.1.

Metropolis-Hastings Algorithm

5.2.3.2.

5.2.3.3.

Convergence Diagnostics

5.3.

Simulation-Based Estimation

5.3.1.

Method of Simulated Moments

5.3.1.1.

Moment Selection

5.3.1.2.

Weighting Matrix Choice

5.3.1.3.

Asymptotic Properties

5.3.2.

Simulated Maximum Likelihood

5.3.2.1.

Likelihood Construction

5.3.2.2.

Simulation Bias

5.3.2.3.

Bias Correction Methods

5.3.3.

Indirect Inference

5.3.3.1.

Auxiliary Model Selection

5.3.3.2.

Binding Function

5.3.3.3.

Asymptotic Theory

5.4.

Bootstrap Methods

5.4.1.

Nonparametric Bootstrap

5.4.1.1.

Bootstrap Samples

5.4.1.2.

Bootstrap Statistics

5.4.2.

Parametric Bootstrap

5.4.2.1.

Model-Based Resampling

5.4.3.

Bootstrap Applications

5.4.3.1.

Confidence Intervals

5.4.3.2.

Hypothesis Testing

5.4.3.3.

Bias Correction

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6. Agent-Based Modeling