This paper has shown how survey micro data can be used to obtain a daily measure of expectations, and how the measurement model can be augmented with indirect measures of expectations that are more regularly observed. Methods for both point and qualitative assessments have been presented. Their usefulness has been demonstrated in several empirical applications, showing that (1) they can provide both more timely and more precise information about expectations than survey-wave-level statistics, that (2) they can be used to identify the impact of major events on expectations, and by (3) exemplifying how the impact of news surprises on expectations can be estimated with their help.
Beyond the applications of this paper, a daily measure may be used to improve the understanding of expectations formation by e.g. allowing us to measure how quickly agents update their information sets after an economic shock, see inter alia Mankiw et al.
(2004) or Coibion and Gorodnichenko (2010). Moreover, it can be used to analyze the role of expectations in the functioning of financial markets at high frequency. My method also serves as a new device to present survey results in a different manner than usual, and may therefore help the interested general public to understand how survey respondents interpret the impact of recent events, such as monetary policy communication or indicator releases.
My method could be extended in several interesting directions. First, modeling the state disturbance (ηtin eq. 1.2) as conditionally heteroscedastic can improve the understanding of the dynamics of uncertainty in a high-frequency context based on direct, survey-based measures, paralleling the vast efforts to estimate and predict the dynamics in the volatil-ity of financial assets (e.g. Kim, Shephard, and Chib, 1998). Second, by relaxing the homoscedasticity assumption for the disturbance of the measurement equation (εt e.g.
in eq. 1.1), I could obtain a daily measure of the disagreement in expectations. Recent research in macroeconomics has employed measures of disagreement to analyze the role of information rigidities in expectations formation (e.g. Mankiw et al., 2004).
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