Research

This paper investigates the nonlinear effects of aggregate demand dynamics over medium and long-term horizons, focusing on whether contractionary aggregate demand shocks have distinct long-lasting impacts compared to expansionary shocks (sign dependence). We begin by identifying a long-term demand shock, termed the 'hysteresis' shock, within a structural vector autoregression framework. To assess sign dependence, we employ local projections with a nonlinear transformation of the shock. This methodology is applied to a quarterly U.S. macroeconomic dataset that includes variables related to the productivity and labor market channels of hysteresis. Our findings indicate that contractionary shocks tend to have stronger and more persistent adverse effects, particularly on productivity-related outcomes, while expansionary shocks gain relevance over time for labor market variables. When disaggregating by demographic groups, we find more pronounced responses among disadvantaged workers, indicating greater sensitivity to both weak aggregate demand and high-pressure economic conditions.

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

Abstract forthcoming.

Presented at: Details forthcoming