# A Sieve-SMM Estimator for Dynamic Models

## TL;DR

This paper introduces a flexible Sieve-SMM estimator for nonlinear dynamic models that accurately estimates parameters and shock distributions without requiring parametric assumptions, improving robustness against misspecification.

## Contribution

It develops a novel Sieve-SMM approach that approximates shock distributions with a Gaussian and tails mixture sieve, extending asymptotic theory to complex models with latent variables.

## Key findings

- Estimator achieves consistency, rate of convergence, and asymptotic normality.
- Application reveals significant reduction in estimated relative risk-aversion.
- Method improves robustness in asset pricing models.

## Abstract

This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required. However, economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification. The Sieve-SMM estimator addresses this issue by flexibly approximating the distribution of the shocks with a Gaussian and tails mixture sieve. The asymptotic framework provides consistency, rate of convergence and asymptotic normality results, extending existing results to a new framework with more general dynamics and latent variables. An application to asset pricing in a production economy shows a large decline in the estimates of relative risk-aversion, highlighting the empirical relevance of misspecification bias.