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基于模拟伪匹配的识别稳健推断AI 解读

We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond the strict and exact…

含 MIDAS 协变量的动态面板：非线性、估计与拟合AI 解读

This paper introduces Mixed Data Sampling (MIDAS) into the panel data context. To address the unidentified nuisance parameter problem, we propose to invert model specification tests for inference on the MIDAS parameter along with bounds tests for model coefficients. Illustrative identification, simulation and empirical analyses are conducted in the dynamic GMM framework. Our framework allows for departures from i.i.d errors such as clustering and dynamic specifications. A…

能源价格动态中时变参数的识别稳健检验AI 解读

SUMMARY We test for the presence of time‐varying parameters (TVP) in the long‐run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean‐reverting models. We also propose residual‐based diagnostic tests and examine out‐of‐sample forecasts. In‐sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is too restrictive to conclusively fit the data. Out‐of‐sample analysis…