In this paper, I propose a flexible non-parametric method using Recurrent Neural Networks(RNN) to estimate the dynamic structure of most expectation formation models in macroeconomics. This approach does not rely on restrictive assumptions of functional forms and parametric methods but 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 find qualitatively new results: (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 future: they behave as if they were adaptive learners in ordinary periods and become forward-looking as the state of economy gets worse; (3) the content of signals on economic condition, rather than the volume of these signals, plays the most important role in creating the attention-shift. My method also allows me to apply the Double Machine Learning method to assess the statistical significance 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 gets worse.
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In light of the experience of the last few years, the object of this paper is to re-examine the role of supply shocks, labour market tightness and expectation formation in explaining bouts of inflation. We begin by showing that a quasi-flat Phillips curve, which was popular prior to the pandemic, still fits the post-2020 US data well with the implication that (1) labour market tightness likely played a limited role in generating recent inflation and (2) changes in short term inflation expectations induced by supply shocks likely played a major role. We then explore how best to capture the join dynamics of inflation and inflation expectations in response to supply shocks. Given the difficulty of capturing these dynamics under rational expectations, we propose and evaluate a model with bounded rationality. In our model, supply shocks that affect many goods affect inflation expectations and this drives persistent inflation dynamics, while supply shocks that are concentrated in a sector lead to much more temporary changes in both inflation and inflation expectations. Although our departure from full rationality is minor, it allows perceived common shocks to dis-aggregated inflation series to be amplified and propagated over time in a manner consistent with observation.

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Expectations about different macroeconomic aspects correlate with each other. Using Michigan Survey of Consumers (MSC), I found consumers' inflation expectation is positively correlated with expectations on unemployment status. Such a correlation is inconsistent with realized data, professionals' belief, and the standard New Keynesian Model. I then perform a structural test in the framework of noisy information model and show that consumers form their expectations on multiple macroeconomic variables jointly rather than independently, thus causing these expectations to be correlated with each other. These results imply the consumers have a subjective model about how macroeconomics variables are correlated that is different from the professionals and the reality. In particular, consumers believe economic conditions will be worse during episode with extensive inflation news, even if there's only mild inflation, causing their average expectation on inflation to co-move with that of unemployment and business condition. These patterns call for explanations on how agents form beliefs on interactions between macroeconomic variables that are different from the actual structure of data. They also suggest Central Bank should use inflation-related expectation management policy with cautious, as such policy may induce pessimistic responses among households.
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India witnessed a sharp wage catch-up by the historically disadvantaged scheduled castes and tribes (SC/STs) towards non-SC/ST levels during the period 1983-2012. We provide a structural explanation for the catch-up, using a multi-sector, heterogenous agent model where individuals differ in ability as well as their caste identity. Castes differ in the costs of schooling and accessing sectoral labor markets which results in caste-based talent misallocations. We show that exogenous productivity growth can explain 72 percent of the observed wage convergence. As in the data, education convergence is the primary driver of the wage convergence in the model. We provide independent evidence in support of this mechanism.
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Over the past three decades India and China have experienced rapid growth and structural transformation. Underneath this similarity however was one significant difference: rural-urban wage gaps declined in India, but widened in China. In both countries, the majority of these wage dynamics are unexplained by worker attributes. We formalize a two-sector-two-location model in which structural transformation and urbanization respond endogenously to productivity shocks. While the structural transformation effect widens the urban-rural wage gap, the urbanization effect reduces it. We attribute the contrasting wage gap dynamics in the two countries to the higher costs of urban relocation for workers in China.
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