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This paper explores the implications of filtering and no-arbitrage for the maximum likelihood estimates of the entire conditional distribution of the risk factors and bond yields in Gaussian macro-finance term structure model (MTSM) when all yields are priced imperfectly. For typical yield curves and macro-variables studied in this literature, the estimated joint distribution within a canonical MTSM is nearly identical to the estimate from an economic-model-free factor…

ABSTRACT We find that several recently proposed consumption‐based models of stock returns, when evaluated using an optimal set of managed portfolios and the associated model‐implied conditional moment restrictions, fail to capture key features of risk premiums in equity markets. To arrive at these conclusions, we construct an optimal Generalized Method of Moments (GMM) estimator for models in which the stochastic discount factor (SDF) is a conditionally affine function of a…

摘要暂缺

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

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…

资产价格马尔可夫模型的模拟矩估计AI 解读

This paper provides a simulated moments estimator of the parameters of dynamic models in which the state vector follows a time-homogeneous Markov process. Conditions are provided for both weak and strong consistency as well as asymptotic normality. Various trade-offs among the regularity conditions underlying the large sample properties of the simulated moments estimator are discussed in the context of an asset-pricing model. Geometric ergodicity of the underlying Markov…

非线性理性预期模型的广义工具变量估计AI 解读

This paper describes a method for estimating and testing nonlinear rational expectations models directly from stochastic Euler equations. The estimation procedure makes sample counterparts to the population orthogonality conditions implied by the economic model close to zero. An attractive feature of this method is that the parameters of the dynamic objective functions of economic agents can be estimated without explicitly solving for the stochastic equilibrium.

非线性理性预期模型的广义工具变量估计AI 解读

This paper describes a method for estimating and testing nonlinear rational expectations models directly from stochastic Euler equations. The estimation procedure makes sample counterparts to the population orthogonality conditions implied by the economic model close to zero. An attractive feature of this method is that the parameters of the dynamic objective functions of economic agents can be estimated without explicitly solving for the stochastic equilibrium.

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小样本因子模型中似然比统计量的解释AI 解读

The use of the likelihood ratio statistic in testing the goodness of fit of the exploratory factor model has no formal justification when, as is often the case in practice, the usual regularity conditions are not met. In a Monte Carlo experiment it is found that the asymptotic theory seems to be appropriate when the regularity conditions obtain and sample size is at least 30. When the regularity conditions are not satisfied, the asymptotic theory seems to be misleading in…