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网络中的动态创新传导：理论、大 T 推断与滞后投入产出转换下的商业周期AI 解读

I develop an econometric framework that rationalizes the dynamics of a cross-sectional variable by lagged transmissions of innovations along bilateral links between units. While nesting the Spatial Autoregression and Spatial Error Model as limits and producing equivalent impulse-responses in the long run, the Network-Vector-Autoregression (NVAR) I propose can accommodate general transmission patterns over time and yields "networked" transition dynamics distinct from those…

离散抽样或时间聚合自回归模型的参数识别与推断AI 解读

I consider an AR($p$) process that is observed every $q$ periods, either as a snapshot (stock variable) or as a sum over the sampling interval (flow variable). I first characterize the resulting ARMA process followed by observables. Under fairly mild assumptions, I then derive the identified set for general lag lengths $p \in \mathbb{N}$ and sampling frequencies $q \in \mathbb{N}$, I bound its cardinality, and I provide an algorithm to compute all candidate points and…

VAR 中宏观经济不稳定性的来源与性质AI 解读

For a general class of dynamic and stochastic structural models, we show that (i) non-linearity in economic dynamics is a necessary and sufficient condition for time-varying parameters (TVPs) in the reduced-form VARMA process followed by observables, and (ii) all parameters' time-variation is driven by the same, typically few sources of stochasticity: the structural shocks. Our results call into question the common interpretation that TVPs are due to "structural…

采用模型渐进过渡的序贯蒙特卡洛方法AI 解读

Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a…