Improved Convergence Rate of Nested Simulation with LSE on Sieve

TL;DR

This paper analyzes the convergence rates of Least Squares Estimators on sieve for nested simulation, showing potential improvements over traditional Monte Carlo and kernel ridge regression methods, with theoretical and numerical validation.

Contribution

It provides asymptotic analysis of LSE on sieve for conditional expectations without restrictive assumptions, identifying conditions for optimal convergence rates.

Findings

Convergence rate can surpass standard Monte Carlo methods.

Conditions identified for achieving the optimal square root convergence rate.

Numerical experiments support theoretical results.

Abstract

Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques. In this paper, we treat conditional expectation as a function of the multidimensional conditioning variable and provide asymptotic analyses of general Least Squared Estimators on sieve, without imposing specific assumptions on the function's form. Our study explores scenarios in which the convergence rate surpasses that of the standard Monte Carlo method and the one recently proposed based on kernel ridge regression. We also delve into the conditions that allow for achieving the best possible square root convergence rate among all methods. Numerical experiments are conducted to support our statements.