Research

Do temporary aggregate demand shocks have lasting effects, and are they asymmetric between contractions and expansions? Using U.S. data from 1983:Q1-2019:Q4, we identify demand shocks with potential long-run consequences via a Bayesian SVAR and trace their propagation with nonlinear local projections. We find that negative shocks dominate in the short run, but positive shocks build up over time and by the medium run generate equally persistent effects on output. We investigate the mechanisms behind this result and argue that positive hysteresis is transmitted primarily through the labor market channel: expansions durably lower long-term unemployment and raise labor force participation. By contrast, the capital accumulation and R&D channels transmit predominantly negative hysteresis.

Presented at: Macroeconometrics in Salerno (2025); 13th SIdE Workshop for PhD students in Econometrics and Empirical Economics (WEEE, 2025); 13^th Conference of the International Association for Applied Econometrics (IAAE 2025, Turin); Trans-Atlantic Doctoral Conference (TADC) at the London Business School (2025)*; 3^rd UEA Time Series Workshop (2025); Junior Milan Time Series Workshop (2025); 17^th UniTO-Collegio Carlo Alberto Ph.D. Workshop in Economics (2025)*.

Abstract forthcoming.

Presented at: Bank of England internal seminar (2025); University of Pisa (2025)*; Workshop on Macroeconomics and Innovation for the Green Transition (2025, Salerno); 18^th International Conference on Computational and Financial Econometrics (CFE 2024, London); University of Florence (2024)*; 12^th Annual Conference of the Italian Association of Environmental and Resource Economists (IAERE 2024, Pescara); 29^th Annual Conference of the European Association of Environmental and Resource Economists (EAERE 2024, Leuven); Econometric Models of Climate Change (EMCC 2024, Cambridge); 4^th Sailing the Macro Workshop (2024, Ortigia).

We propose a new impulse response matching procedure for estimating the parameters of a dynamic stochastic general equilibrium (DSGE) model from observed macroeconomic time series. Our estimator hinges on an indirect inference approach in which the auxiliary model is a structural vector autoregressive (SVAR) model. The SVAR model is identified using independent component analysis. A specificity of our approach is that, by using a minimum distance index, we exploit the non-Gaussianity of the observed data, but we allow the model-simulated data to be Gaussian. We derive the asymptotic properties of the estimator and we conduct a Monte Carlo simulation to study the performance of the proposed procedure. Finally, we present an application to a simple New Keynesian DSGE model.

Presented at: 35^th EC² Conference; 12^th Conference of the International Association for Applied Econometrics (IAAE 2024, Tessaloniki)*; 8^th RCEA Time Series Econometrics Workshop (2025, London)*; 17^th International Conference on Computational and Financial Econometrics (CFE 2023, Berlin); Italian Congress of Econometrics and Empirical Economics (ICEEE 2023).

Abstract forthcoming.

Presented at: EAEPE Annual Conference (2025, Athens); 13^th Annual Conference of the Italian Association of Environmental and Resource Economists (IAERE 2025, Rome).

Abstract forthcoming.

Presented at: EAEPE Annual Conference (2025, Athens)*.