MEASUREMENT ERRORS IN DYNAMIC MODELS
研究了动态模型中存在测量误差时的参数识别问题，特别是当测量误差存在序列相关时，给出了有限信息识别的阶条件和秩条件，并讨论了单方程、向量自回归和面板数据模型的应用。
Static models that are not identifiable in the presence of white noise measurement errors are known to be potentially identifiable when the model has dynamics. However, few results are available for the plausible case of serially correlated measurement errors. This paper provides order and rank conditions for “limited information” identification of parameters in dynamic models with measurement errors where some aspects of the probability model are not fully specified or utilized. The key is to consider a model for the contaminated data that has richer dynamics than the model for the correctly observed data. Simply counting the total number of unknown parameters in the true model relative to the estimable model will not yield an informative order condition for identification. Implications for single-equation, vector autoregressive, and panel data models are studied.