Efficient Estimation of Linear Asset-Pricing Models with Moving Average Errors
深入探讨对数线性跨期资本资产定价模型的条件矩约束，指出常用GMM估计量无效，推导了时间加总消费下的矩条件，并给出了偏好参数的有效CNN估计量及其效率边界。
This paper explores in depth the nature of the conditional moment restrictions implied by log-linear intertemporal capital asset pricing models (ICAPMs) and shows that the generalized instrumental variables (GMM) estimators of these models (as typically implemented in practice) are inefficient.The moment conditions in the presence of temporally aggregated consumption are derived for two log-linear ICAPMs.The first is a continuous time model in which agents maximize expected utility.In the context of this model, we show that there are important asymmetries between the implied moment conditions for infinitely and finitely-lived securities.The second model assumes that agents maximize non-expected utility, and leads to a very similar econometric relation for the return on the wealth portfolio.Then we describe the efficiency bound (greatest lower bound for the asymptotic variances) of the CNN estimators of the preference parameters in these models.In addition, we calculate the efficient CNN estimators that attain this bound.Finally, we assess the gains in precision from using this optimal CNN estimator relative to the commonly used inefficient CMN estimators.