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利用离散时间观测估计连续时间线性 DSGE 模型AI 解读

We provide a general state space framework for estimation of the parameters of continuous-time linear DSGE models from discrete-time data. Our approach relies on the exact discrete-time representation of the equilibrium dynamics, hence avoiding discretization errors. We construct the exact likelihood for data sampled either as stocks or flows, based on the Kalman filter, and provide necessary and sufficient conditions for local identification of the frequency-invariant…

消费资本资产定价模型估计中的比索问题AI 解读

This paper shows that the consumption‐based capital asset pricing model (C‐CAPM) with low‐probability disaster risk rationalizes pricing errors. We find that implausible estimates of risk aversion and time preference are not puzzling if market participants expect a future catastrophic change in fundamentals, which just happens not to occur in the sample (a “peso problem”). A bias in structural parameter estimates emerges as a result of pricing errors in quiet times. While…