Learning and Subjective Expectation Formation: A Recurrent Neural Network Approach

Abstract

I propose a flexible non-parametric method using Recurrent Neural Networks (RNN) to estimate a generalized model of expectation formation. This approach does not rely on restrictive assumptions of functional forms and parametric methods yet nests the standard approaches of empirical studies on expectation formation. Applying this approach to data on macroeconomic expectations from the Michigan Survey of Consumers (MSC) and a rich set of signals available to U.S. households, 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: they behave as adaptive learners in ordinary periods and become forward-looking as the state of economy gets worse; (3) the content of signals on economic conditions, rather than the amount of news coverage on these signals, plays the most important role in creating the attention-shift. Double Machine Learning approach is then 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.