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分离偏好与误差：二元面板数据模型AI 解读

In many stated choice experiments researchers observe the random variables V t , X t , and Y t = 1{ U + δ ⊤ X t + ε t < V t }, t ≤ T , where δ is an unknown parameter and U and ε t are unobservable random variables. We show that under weak assumptions the distributions of U and ε t and also the unknown parameter δ can be consistently estimated using a sieved maximum likelihood estimation procedure.

含潜在时间序列的非参数回归AI 解读

In this paper we investigate a class of semi-parametric models for panel data sets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecasted along with a non-parametric covariate effect. Our model is motivated by the need to be flexible with regard to the functional form of covariate effects but also the need to be practical with regard to forecasting of time series effects. We propose…