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.

Awards: Honorable mention at the the International Association for Applied Econometrics (IAAE), 2025.

Presented at: European Central Bank (2025); 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); 8^th Conference on 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).

We quantify the macroeconomic effects of EU green spending under Cohesion Policy. We focus on the share of European Structural and Investment (ESI) Funds targeting the low-carbon economy. We construct a new harmonized green disbursement series by integrating project-level allocations across funds and programming cycles, classifying projects with priority-theme and intervention-code information. We link these series to regional and national outcomes in a NUTS2 dataset spanning 2007–2023. Exploiting regional variation, we estimate dynamic effects using an instrumental variable local projection design. Preliminary results point to stimulative effects of Green ESI spending: it lifts regional GDP, private investment, and green innovation, proxied by green patents. At the country level, it increases private investment in climate-change mitigation. Our study provides the first EU-wide evidence on the economic impact of cohesion-funded green investment.

We propose Years of Lost Growth (YOLG), an intuitive metric that tells how many years of growth would be needed for a country to return to the GDP level that would have prevailed in the absence of climate change. Using a global panel of countries from 1960–2019, we estimate the nonlinear response of GDP per capita growth to temperature shocks. We then project GDP with and without climate change to 2100 under multiple warming and socioeconomic pathways, and compute country-specific YOLG. Expressing impacts as years lost, rather than GDP percentages, reveals sharper cross-country heterogeneity: warmer, low-growth economies incur larger losses, while benefits in cooler, high-growth economies are attenuated. We show that YOLG is transparent, easy to communicate, and a practical complement to conventional metrics of the macroeconomic effects of climate change.

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

Do temperature shocks affect economic activity? And for how long? We show that answers depend on (i) how shocks are defined and (ii) the econometric method used. We construct several shock measures from the literature and document their serial correlation. We then run Monte Carlo simulations calibrated to macro-climate settings—with panel structure and nonlinearities to capture climate-dependent effects—to generate impulse responses with varying long-run behaviour. Varying shock persistence, we compare Local Projections (LP), Vector Autoregressions (VAR), and Autoregressive Distributed Lags (ARDL). When shocks are serially correlated, LPs inherit that persistence and generate greater long-run effects, while ARDLs correct for shock serial correlation. Both LP and ARDL are less biased than VAR at long horizons. Finally, re-estimating influential studies, we show that the choice of shock definition and estimator can materially affect conclusions about long-run GDP effects.

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