Minimum Distance Estimation of Possibly Noninvertible Moving Average Models
研究了非高斯误差下移动平均模型的估计方法，利用高阶累积量识别参数，无需假设模型可逆，并提出了模拟估计量，在Fama-French组合收益中发现了不可逆性。
This article considers estimation of moving average (MA) models with non-Gaussian errors. Information in higher order cumulants allows identification of the parameters without imposing invertibility. By allowing for an unbounded parameter space, the generalized method of moments estimator of the MA(1) model is classical root- T consistent and asymptotically normal when the MA root is inside, outside, and on the unit circle. For more general models where the dependence of the cumulants on the model parameters is analytically intractable, we consider simulation-based estimators with two features. First, in addition to an autoregressive model, new auxiliary regressions that exploit information from the second and higher order moments of the data are considered. Second, the errors used to simulate the model are drawn from a flexible functional form to accommodate a large class of distributions with non-Gaussian features. The proposed simulation estimators are also asymptotically normally distributed without imposing the assumption of invertibility. In the application considered, there is overwhelming evidence of noninvertibility in the Fama-French portfolio returns.