Unbiased Estimators of Long-Run Expected Rates of Return
长期预期收益率的无偏估计量AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要直接研究股票和债券相关收益分布下的长期预期收益评估与估计。
反馈分类问题长期预期收益率的无偏估计量AI 解读 ↓
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This article documents the biases in using sample arithmetic or geometric means of one-period returns to assess long-run expected rates of return. The formulas developed are applicable to other compound growth processes. For types of distributions of one-period returns likely to be encountered for bonds and stocks, numerical values for these biases are given. Four unbiased estimators of long-run expected rates of return are developed and their relative efficiency examined.
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多重门槛回归的 Lasso 后推断及收益可预测性应用
This article revisits the multiple threshold regression model, wherein coefficient parameters shift across regimes in accordance with the value of a threshold variable. We focus on developing a post-Lasso inference framework to resolve the selection uncertainty—particularly pronounced when threshold effects are weak—intrinsic to the group Lasso-based procedure that jointly estimates the number and locations of multiple thresholds. This objective is accomplished by…
分位数回归的固定平滑一致推断
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时变可预测性的稳健推断:Sieve-IVX 方法
Predictive regression models are often used to evaluate the predictive capability of economic fundamentals on bond and equity returns. Inferential procedures in these regressions typically employ parameter constancy or piecewise constancy in slope coefficients. Such formulations are prone to misspecification, more especially during periods of disturbance or evolution in prevailing economic and financial conditions, which can lead to size distortion and spurious evidence of…
网络回归与监督式中心性估计
The centrality in a network is often used to measure nodes’ importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent…