Time domain estimation of non-fundamental ARMA models in the presence of heteroskedasticity of unknown form
提出一种时域最小距离估计方法，用于估计可能存在非基础性的非高斯线性ARMA模型，该方法不依赖对波动过程的具体参数化，适用于条件异方差形式未知的情况，并应用于29个OECD国家的通胀数据发现普遍的非基础性证据。
This paper considers time domain estimation of possibly non-fundamental (that is, non-causal and/or non-invertible) non-Gaussian linear ARMA models with martingale difference innovations that may display conditional heteroskedasticity of unknown form. Instead of explicitly parametrizing the underlying volatility process (the higher order dependence) and employing maximum likelihood procedures, we propose a time domain minimum distance objective function based on innovations predictability using second and third powers of past innovations. Using the proposed efficient GMM estimator, which is consistent and asymptotically normal, we estimate possibly non-fundamental ARMA models to inflation data from 29 OECD countries and find widespread evidence on non-fundamentalness.