Research

Publications

  • Identification of structural VAR models via Independent Component Analysis: a performance evaluation study (with A. Moneta). Journal of Economic Dynamics and Control (2022).

    • Link Publication
    • Working Paper
    • View Abstract Independent Component Analysis (ICA) is a statistical method that linearly transforms a random vector. Under the assumption that the observed data are mixtures of non-Gaussian and independent processes, ICA is able to recover the underlying components, but with a scale and order indeterminacy. Its application to structural vector autoregressive (SVAR) models allows the researcher to recover the impact of independent structural shocks on the observed series from estimated residuals. We analyze different ICA estimators, recently proposed within the field of SVAR analysis, and compare their performance in recovering structural coefficients. Moreover, we assess the size distortions of the estimators in hypothesis testing. We conduct our analysis by focusing on non-Gaussian distributional scenarios that get gradually close to the Gaussian case. The latter is the case where ICA methods fail to recover the independent components. Although the ICA estimators that we analyze show similar pattern of performance, two of them — the fastICA algorithm and the pseudo-maximum likelihood estimator — tend to perform relatively better in terms of variability, stability across sub- and super-Gaussian settings, and size distortion. We finally present an empirical illustration using US data to identify the effects of government spending and tax cuts on economic activity, thus providing an example where ICA techniques can be used for hypothesis testing.

  • Does public R&D funding crowd-in private R&D investment? Evidence from military R&D expenditures for US states (with E. Russo, A. Roventini). Research Policy (2023).

    • Link Publication
    • Working Paper
    • View Abstract Defense R&D represents the largest component of US public R&D spending and historically has promoted a wide range of civilian innovations. However, the empirical evidence on the impact of defense R&D is scant and it does not provide conclusive results on the possible crowding-in (-out) effects on private R&D investment. Exploiting a longitudinal dataset linking public R&D obligations to private R&D expenditures for US states, we investigate the impact of defense R&D on privately-financed R&D. To address potential endogeneity in the allocation of funds, we use an instrumental variable identification strategy leveraging the differential exposure of US states to national shocks in federal military R&D. We document considerable crowding-in effects with elasticities in the 0.11–0.14 range. These positive effects extend also to the labor market, when focusing on employment in selected R&D intensive industries and especially for engineers.

Working Papers

  • Calibration and Validation of Macroeconomic Simulation Models: A General Protocol by Causal Search (with M. Martinoli, A. Moneta). Under Review .

    • Working Paper
    • View Abstract We introduce a general procedure for models’ calibration and validation. Configurations of parameters are selected on the basis of a loss function involving a distance between model-derived structural coefficients and their empirical counterparts. These, in both cases, are locally identified by exploiting non-Gaussianity in a structural vector autoregressive framework under a data-driven approach. We use model confidence set to account for the uncertainty in the selection procedure. We provide a measure of validation by comparing (model’s and empirical) shocks-variables structure. We apply our procedure to a complex macroeconomic simulation model that studies the link between climate change and economic growth.

  • Robust-less-fragile: Tackling Systemic Risk and Financial Contagion in a Macro Agent-Based Model (with M. Guerini, M. Napoletano & A. Roventini). Under Review .

    • Working Paper
    • View Abstract We extend the Schumpeter meeting Keynes (K+S; see Dosi et al., 2010, 2013, 2015) to model the emergence and the dynamics of an interbank network in the money market. The extended model allows banks to directly exchange funds,while evaluating their interbank positions using a network-based clearing mechanism (NEVA, see Barucca et al., 2020). These novel adds on, allow us to better measure financial contagion and systemic risk events in the model and to study the possible interactions between micro-prudential and macro-prudential policies. We find that the model can replicate new stylized facts concerning the topology of the interbank network, as well as the dynamics of individual banks’ balance sheets. Policy results suggest that the economic system at large can benefit from the introduction of a micro-prudential regulation that takes into account the interbank network relationships. Such a policy decreases the incidence of systemic risk events and the bankruptcies of financial institutions. Moreover, a trade-off between financial stability and macroeconomic performance does not emerge in a two-pillar regulatory framework grounded on i) a Basel III macro-prudential regulation and ii) a NEVA-based micro-prudential policy. Indeed, the NEVA allows the economic system to achieve financial stability without overly stringent capital requirements.

Projects

  • Creating Jobs Out of the Green: The Employment Effects of the Energy Transition (with E. Cappa, F. Lamperti).

  • Revisiting the Role of the Fiscal Multiplier in a Monetary Union.