A Scrambled Method of Moments

Abstract

Quasi-Monte Carlo (qMC) methods are a powerful alternative to classical Monte-Carlo (MC) integration. Under certain conditions, they can approximate the desired integral at a faster rate than the usual Central Limit Theorem, resulting in more accurate estimates. This paper explores these methods in a simulation-based estimation setting with an emphasis on the scramble of Owen (1995). For cross-sections and short-panels, the resulting Scrambled Method of Moments simply replaces the random number generator with the scramble (available in most softwares) to reduce simulation noise. Scrambled Indirect Inference estimation is also considered. For time series, qMC may not apply directly because of a curse of dimensionality on the time dimension. A simple algorithm and a class of moments which circumvent this issue are described. Asymptotic results are given for each algorithm. Monte-Carlo examples illustrate these results in finite samples, including an income process with "lots of heterogeneity."

Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo

“…In the case where F is finitedimensional, the discrepancy above can be thought of as comparing a finite number of summary statistics of P and Q, as commonly done for the method of simulated moments or in ABC. For this case, the use of QMC was previously studied in [32]. In contrast, our work will focus on the most common discrepancies based on infinite-dimensional F, which we introduce below.…”