Learning and Subjective Expectation Formation: A Recurrent Neural Network Approach

Most empirical studies on expectation formation models share a common dynamic structure but impose different functional form restrictions. I propose a flexible non-parametric method that maintains this dynamic structure to estimate a model of expectation formation using Recurrent Neural Networks. Applying this approach to data on macroeconomic expectations from the Michigan Survey of Consumers and a rich set of signals, I document three novel findings: (1) agents’ expectations about the future economic condition have asymmetric and non-linear responses to signals; (2) agents’ attentions shift from signals about the current state to signals about the future as the economic condition deteriorates ; (3) the content of signals on economic conditions plays the most important role in creating the attention-shift. Double Machine Learning approach is used to obtain statistical inferences of these empirical findings. Finally, I show these stylized facts can be generated by a model with rational inattention, in which information endogenously becomes more valuable when economic status worsens.