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.
翻译:本文建议用一个Sieve-SMM(Sieve-SMM)模拟模型来估计非线性动态模型中的震荡参数和分布,这些模型的可能性和时间是无法移动的。与SMM(样本与模拟瞬时相匹配)相比,SMM(SmM)的一个重要关切是,需要有一个参数分布。然而,依赖这种分布的经济数量,如福利和资产价格,可能会敏感到不精确的特性。Sieve-SMM(SimmM)估计解决这个问题的方法是灵活地与高斯和尾巴混合物的静态模型对震荡分布进行对接。无线性框架提供了一致性、趋同率和无线性正常性结果,将现有结果扩展到一个具有更一般动态和潜在变量的新框架。在生产经济中资产定价的应用表明相对风险反常估计值的大幅下降,突出了误差偏差的经验相关性。