Research

Soft-Landing or Inflation Scares:  Is it good fortune or good policy?

• • We discuss the timing and strength of the Fed's reaction to the recent inflation surge within an estimated micro-founded macroeconomic model with information frictions. As a result of these frictions, subjective inflation expectations can persistently drift away from the target.  We obtain a closed-form solution that we estimate using Bayesian techniques on both US macroeconomic time series and forecast data from the Survey of Professional Forecasters. Counterfactual simulations suggest that the timing --  rather than the strength --  of the policy reaction to this inflation surge is critical for anchoring inflation expectations and preventing the entrenchment of inflation. We show that the Fed fell behind the curve in 2021  since an earlier tightening could have reduced the inflation peak without triggering a recession. However, further delays would have unanchored inflation expectations, strengthened the inflation surge, and entailed larger output losses.

Subjective Models of the Macroeconomy and Average Inflation Targeting

Social learning expectations: microfoundations and a Dynare toolbox

• • Social learning (SL) is a behavioral model in which expectations and the resulting aggregate dynamics stem from the interactions of a large amount of heterogeneous agents. Nonetheless, this framework has so far lacked micro-foundations and a general-solution method. This paper bridges these two gaps with: (i) a micro-founded New Keynesian model with social learning expectations; (ii) a general solution method that we implement in a Dynare toolbox that solves any linear state-space model with SL expectations. The resulting framework provides a self-contained tool to contrast policy analysis under SL and rational expectations. As an illustration, optimal monetary policy rules are studied under the two expectation regimes.

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Unemployment risk and discretionary fiscal spending

• • This paper studies the effects of discretionary fiscal policy responses to adverse aggregate shocks. For this, I build a tractable model where households face idiosyncratic unemployment risk in a Search-and-Matching (SaM) labor market with explicit intensive and extensive employment margins. Focusing on the spending side of fiscal stimuli, I investigate transitory increases in Unemployment Insurance (UI) benefits and public purchases. I show that the effects of transitory increases in fiscal spending largely depend on the state of the labor market and the type of adverse shock hitting the economy. At the aggregate level, the most welfare-improving fiscal stimuli appear to be rather small and over a long period. At the idiosyncratic level, welfare improvements are very unequally distributed.  Front-loaded increases in fiscal spending may run into supply constraints and have important undesirable consequences. Fiscal stimuli through UI transfers are never Pareto efficient whereas fiscal stimuli through public purchases can be.  

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Precautionary saving and un-anchored expectations
(previously titled : Learning and supply shocks in a HANK economy)

• • This paper investigates monetary policy in a heterogeneous agent new Keynesian (HANK) model where agents face idiosyncratic income risk and use adaptive learning in order to form their expectations. Households experience different histories and observe different idiosyncratic variables.  This gives rise to idiosyncratic learning processes, which naturally implies the existence of heterogeneous expectations. In HANK models, supply shocks generate precautionary saving. The learning setup amplifies this effect and can result in long-lasting disinflationary traps. Dovish Taylor rules focused on closing the output gap dampen the learning effects. Price level targeting improves the inflation and output stabilization trade-off by better anchoring expectations.

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Work in progress:

• Equilibrium selection and idiosyncratic information set in an Aiyagari economy

• Inflation target and misspecified equilibrium at the effective lower bound