Variance Reduction Techniques for Simulation-Based Estimation

Описание: Efficient simulation is at the heart of structural estimation. In this video, we take a step back from the estimation algorithms themselves and focus on the simulation engine: asking whether we can get the same accuracy with fewer draws.

Slides used in the video are available here: https://raw.githack.com/tyleransom/st...
Source code for the slides is here: https://github.com/tyleransom/structu...

We cover:
Why purely random (Monte Carlo) draws are inefficient and tend to produce "clumpy" coverage of the sample space
How to generate common error distributions: type I extreme value from uniform draws, and multivariate normal via Cholesky decomposition
Antithetic draws (antithetic variates)
Halton sequences: a deterministic, prime-number-based method for uniformly filling multi-dimensional spaces
Practical guidelines for simulation-based estimation: using the same draws across parameter evaluations to prevent "chatter," when to increase R with sample size (SML vs. SMM), and how to choose among methods

These variance reduction techniques allow researchers to achieve equivalent estimator accuracy with substantially fewer simulation replications, reducing both memory and computational costs.

Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!

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