Abstract This paper incorporates diagnostic expectations into a general equilibrium macroeconomic model with a financial intermediary sector. Diagnostic expectations are a forward-looking model of extrapolative expectations that overreact to recent news. Frictions in financial intermediation produce non-linear spikes in risk premia and slumps in investment during periods of financial distress. The interaction of sentiment with financial frictions generates a short-run amplification effect followed by a long-run reversal effect, termed the feedback from behavioural frictions to financial frictions. The model features sentiment-driven financial crises characterized by low pre-crisis risk premia and neglected risk. The conflicting short-run and long-run effect of sentiment produces boom–bust investment cycles. The model also identifies a stabilizing role for diagnostic expectations. Under the baseline calibration, financial crises are less likely to occur when expectations are diagnostic than when they are rational.

Abstract By augmenting the continuous-time specification of Harris and Laibson (2013) with the assumption that hard borrowing constraints do not bind in equilibrium, present bias can be tractably incorporated into rich consumption-saving models featuring stochastic income, risky and illiquid assets, and costly borrowing. I present closed-form expressions characterizing how present bias affects consumption, illiquid asset demand, and welfare. This welfare analysis specifies the channels through which present bias can matter for policy and uncovers the present-bias dilemma: present bias can have large welfare costs, but individuals have little ability to alleviate these costs using financial commitment devices like illiquid assets.

Abstract We estimate β-δ time preferences and relative risk aversion (RRA) using a lifecycle model including stochastic income, liquid and illiquid assets, credit cards, dependents, Social Security, mortality, and bequests. Preference parameters are identified by cross-tabulating four lifecycle age intervals and four balance sheet moments: the share of households carrying (ie, revolving) credit card debt, average carried credit card debt, average net wealth among households carrying credit card debt, and average net wealth among households not carrying credit card debt. The 16 moments are approximately matched by (MSM) parameter estimates β=0.53, δ=0.99, and RRA = 1.9.