Nonparametric stochastic discount factor decomposition
Nonparametric stochastic discount factor decomposition
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非参数随机折扣因子分解
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
T. Christensen
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作者：
T. Christensen
我们引入计量经济学方法来进行估计和推断的永久性和暂时性的组成部分的随机贴现因子（SDF）在动态马尔可夫环境。该方法是非参数的，因为它不对状态过程的运动规律施加参数限制。我们提出了筛选估计的特征值特征函数对，用于分解成其永久性和暂时性的组成部分，以及估计的长期收益率和熵的永久性组成部分的SDF，允许各种各样的经验相关的设置。建立了一致性和收敛速度。当SDF是可观测的时，特征值、产量和熵的估计是渐近正态的和半参数有效的。我们还介绍了非参数估计的延续值下的Epstein-Zin偏好，从而扩展我们的估计的范围，一类重要的递归偏好。估计是简单的实现，在模拟中表现良好，并可用于数值计算的特征函数及其特征值在完全指定的模型时，解析解不可用。
We introduce econometric methods to perform estimation and inference on the permanent and transitory components of the stochastic discount factor (SDF) in dynamic Markov environments. The approach is nonparametric in that it does not impose parametric restrictions on the law of motion of the state process. We propose sieve estimators of the eigenvalue-eigenfunction pair which are used to decompose the SDF into its permanent and transitory components, as well as estimators of the long-run yield and the entropy of the permanent component of the SDF, allowing for a wide variety of empirically relevant setups. Consistency and convergence rates are established. The estimators of the eigenvalue, yield and entropy are shown to be asymptotically normal and semiparametrically efficient when the SDF is observable. We also introduce nonparametric estimators of the continuation value under Epstein-Zin preferences, thereby extending the scope of our estimators to an important class of recursive preferences. The estimators are simple to implement, perform favorably in simulations, and may be used to numerically compute the eigenfunction and its eigenvalue in fully specified models when analytical solutions are not available.