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状态密度时间序列的非平稳性AI 解读

This paper proposes a new framework to analyze the nonstationarity in the time series of state densities, representing either cross-sectional or intra-period distributions of some underlying economic variables. We regard each state density as a realization of Hilbertian random variable, and use a functional time series model to fit a given time series of state densities. This allows us to explore various sources of the nonstationarity of such time series. The potential unit…

模糊性重要吗？多重先验递归效用资产定价模型的估计AI 解读

This paper considers asset pricing models with stochastic differential utility incorporating decision makers׳ concern with ambiguity on true probability measure. Under a representative agent setting, we empirically evaluate alternative preference specifications including a multiple-priors recursive utility. We find that relative risk aversion is estimated around 1–8 with ambiguity aversion and 7.4–15 without ambiguity aversion. Estimated ambiguity aversion is both…

连续状态分布的函数型回归AI 解读

In this paper, we consider a regression model to study the distributional relationship between economic variables. Unlike the classical regression dealing exclusively with mean relationship, our model can be used to analyze the entire dependent structure in distribution. Technically, we treat density functions as random elements and represent the regression relationship as a compact linear operator in the Hilbert spaces of square integrable functions. We propose a consistent…

非平稳二元选择AI 解读

This paper develops an asymptotic theory for time series binary choice models with nonstationary explanatory variables generated as integrated processes. Both logit and probit models are covered. The maximum likelihood (ML) estimator is consistent but a new phenomenon arises in its limit distribution theory. The estimator consists of a mixture of two components, one of which is parallel to and the other orthogonal to the direction of the true parameter vector, with the…