Introduction
MATLAB Workshop
Asymptotic Foundations
Sampling Distributions and Estimator Properties
Estimator Sampling Distribution
Estimator Statistical Property: Unbiasedness
Estimator Statistical Property: Consistency
Estimator Statistical Property: Asymptotic Normality
Inference
Confidence Interval Estimation
Linear Regression: Functions and Assumptions
No Measurement Error Assumption
No Serial Correlation Assumption
Monte Carlo Integration
Random Variate Generation
Inverse Transform Sampling: Theory
Inverse Transform Sampling: Empirical Application
Rejection Sampling: Gaussian-Oscillatory Function
Quasi-Random Sampling: Test Functions
Importance Sampling: Tail Probability Estimation
Importance Sampling: Security Pricing Application
Markov Chain Monte Carlo
Discrete and Continuous Stochastic Processes — Work in progress
Foundational Theory — Work in progress
Metropolis–Hastings Algorithm — Work in progress
Gibbs Sampler — Work in progress
Bayesian Linear Regression — Work in progress
Bayesian Mixed Logit Model — Work in progress
Simulation-Based Estimation and Inference
Maximum Simulated Likelihood: Theory
Maximum Simulated Likelihood: Empirical Application
Method of Simulated Moments: Theory — Work in progress
Method of Simulated Moments: Empirical Application — Work in progress
Bootstrap Methods
Bootstrap: Theoretical Foundations
Bootstrap: Bias-Corrected and Accelerated — Work in progress
Bootstrap: Residual Resampling — Work in progress
Bootstrap: Score Bootstrap — Work in progress
Supporting Functions for Exercises
Ordinary Least Squares (OLS) Estimation Routine
Heteroskedasticity-Robust OLS Estimation Routine
Notation
Mathematical notation and symbols used in this work can be found in the Notation Summary
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