Behavioral expectations improve DSGE/HANK model accuracy
A new Bank of England working paper introduces a flexible method to incorporate behavioral expectations into DSGE and HANK models. The approach significantly improves empirical fit and impulse response functions when applied to US business cycle data.
Flexible deviation from rational expectations
This paper by Jamie Lenney and Biagio Rosso proposes a flexible method to integrate deviations from full information rational expectations (FIRE) within DSGE and HANK models, utilizing the sequence space framework.
The authors implement a reduced-form behavioral expectations process, allowing agents to simultaneously overreact or underreact to current economic conditions and underreact to new information.
This behavioral expectations solver is deployable across a broad spectrum of DSGE and HANK models, encompassing various existing expectations models as special cases.
The method remains agnostic about the precise origins of belief frictions, offering a versatile tool for macroeconomic analysis.
Its application to a medium-scale two-asset HANK model demonstrates its practical utility and empirical strength.
Enhanced empirical fit and impulse responses
The researchers apply their novel approach to a medium-scale two-asset HANK model, jointly estimating both dynamic and behavioral parameters using US business cycle data, including inflation expectation data.
The study's findings reveal that incorporating behavioral expectations quantitatively enhances the empirical fit of the model.
Furthermore, it improves the qualitative properties of its impulse response functions, offering a more nuanced understanding of how economic shocks propagate through the system.
This suggests that accounting for non-rational expectations can provide a more accurate representation of real-world economic dynamics compared to traditional FIRE assumptions.
A step towards realistic modeling
This working paper offers a crucial step towards more realistic macroeconomic modeling by moving beyond rigid rational expectations.
While the approach is flexible and robust, its real-world policy implications will depend on further calibration and validation across diverse economies.
Nevertheless, it provides a powerful framework for central banks to better understand and forecast economic behavior.