We propose a new method to explore the information content of fixed-event forecasts and estimate structural parameters that are keys to sticky and noisy information models. Estimation follows a regression-based framework in which estimated coefficients map one-to-one with parameters that measure the degree of information rigidity. The statistical characterization of regression errors explores the laws that govern expectation formation under sticky and noisy information, that is, they are coherent with the theory. This strategy is still unexplored in the literature and potentially enhances the reliability of inference results. The method also allows linking estimation results to the signal-to-noise ratio, an important parameter of noisy information models. This task cannot be accomplished if one adopts an “agnostic” characterization of regression errors. With regard to empirical results, they show a substantial degree of information rigidity in the countries studied. They also suggest that the theoretical characterization of regression errors yields a more conservative picture of the uncertainty surrounding parameter estimates.

我们提出了一种新方法来探索固定事件预测的信息内容并估计结构参数，这些参数是粘性和噪声信息模型的关键。估计遵循基于回归的框架，其中估计系数与衡量信息刚性程度的参数一一对应。回归误差的统计表征探索了在粘性和噪声信息下控制期望形成的规律，即它们与理论是一致的。这种策略在文献中仍未被探索，并有可能提高推理结果的可靠性。该方法还允许将估计结果与信噪比联系起来，信噪比是噪声信息模型的一个重要参数。如果采用回归误差的“不可知”特征，则无法完成此任务。就实证结果而言，它们表明所研究的国家具有相当程度的信息刚性。他们还表明，回归误差的理论特征产生了关于参数估计的不确定性的更保守的图景。