2019/11/20 by Jean-Jacques Forneron, Forneron, Jean-Jacques
Economics, Econometrics and Finance · Mathematics · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Mathematical Approximation and Integration #Methodology (stat.ME) #Monetary Policy and Economic Impact
paper · pdf · doi:10.48550/arxiv.1911.09128
openalex publication_date 2019/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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."