| 研究実績の概要 |
This project proposes a novel mechanism based on behavioral ingredients and information frictions and applies it to widely used dynamic macroeconomic models. First, we incorporate diagnostic expectations into linear DSGE models. We show that diagnostic expectations generate extra amplification in the presence of nominal frictions; a fall in aggregate supply generates a Keynesian recession; fiscal policy is more effective at stimulating the economy. We perform Bayesian estimation of a rich medium-scale model that incorporates consensus forecast data. Our estimate of the diagnosticity parameter is in line with previous studies. Moreover, we find empirical evidence in favor of the diagnostic model. Diagnostic expectations offer new propagation mechanisms to explain fluctuations. Second, we incorporate recent advances in information processing and assess its importance under alternative informational assumptions by estimating a permanent income consumption model using data for the US economy and show that the misperception model outperforms the rational learning model to explain the movements in consumption and productivity in our sample. Third, we employ unique state-level data to offer insights into how to interpret the relationship between consumer confidence and economic activity. Our results confirm the main finding of Barsky and Sims (2012) that this relationship is predominantly driven by news about the future.
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