Dynamic Censored Regression and the Open Market Desk Reaction Function
为包含滞后因变量的动态时间序列删失回归模型建立了正式渐近理论，证明了条件最大似然估计和最小绝对离差估计的正确性，并应用于公开市场操作台的临时购买行为分析。
The censored regression model and the Tobit model are standard tools in econometrics. This paper provides a formal asymptotic theory for dynamic time series censored regression when lags of the dependent variable have been included among the regressors. The central analytical challenge is to prove that the dynamic censored regression model satisfies stationarity and weak dependence properties if a condition on the lag polynomial holds. We show the formal asymptotic correctness of conditional maximum likelihood estimation of the dynamic Tobit model, and the correctness of Powell's least absolute deviations procedure for the estimation of the dynamic censored regression model. The paper is concluded with an application of the dynamic censored regression methodology to temporary purchases of the Open Market Desk. This article has supplementary material online.