Efficient Estimation of Linear Asset-Pricing Models With Moving Average Errors
具有移动平均误差的线性资产定价模型的有效估计AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确以对数线性跨期资产定价模型作为方法比较的原型,并开展该模型的实证调查,因此归入资产定价方向。
具有移动平均误差的线性资产定价模型的有效估计AI 解读 ↓
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This paper studies alternative methods for estimating parameters from multiperiod conditional moment restrictions. The authors' discussion is couched in the context of a multivariate linear time series model and they use the log-linear intertemporal asset pricing model as a prototype when comparing alternative econometric methods. They propose a generalized method of moments estimator that is scale invariant and is asymptotically equivalent to one used previously in empirical work on asset pricing. The authors then show how to improve the efficiency of this estimator. Finally, they apply these methods in an empirical investigation of the log-linear intertemporal asset pricing model.
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Investment-based asset pricing models typically predict a close link between a firm’s stock return and its characteristics at any point in time. Yet, previous studies have primarily focused on the weaker prediction that this link holds on average, finding substantial empirical support. We show how to incorporate the time-series predictions in the estimation and testing of investment-based models using the generalized method of moments. We find that standard specifications of…
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