Mattia Guerini, Francesco Lamperti and Andrea Mazzocchetti

Author Archive | Mattia Guerini, Francesco Lamperti and Andrea Mazzocchetti

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Unconventional Monetary Policy: Between the Past and Future of Monetary Economics

In this paper we discuss some of the monetary policy issues that have involved major central banks worldwide since the 2008 financial crisis, and which remain open. We provide an excursus of the unconventional monetary policies adopted by central banks in the last decade, focusing on the European Central Bank and the Federal Reserve, and […]

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working_paper_2017_25_cover

Validation of Agent-Based Models in Economics and Finance

Since the influential survey by Windrum et al. (2007), research on empirical validation of agent-based models in economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated critical review of existing validation techniques. We sketch a simple theoretical […]

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working_paper_2017_01_cover

The Janus-Faced Nature of Debt: Results from a Data-Driven Cointegrated SVAR Approach

In this paper, we investigate the causal effects of public and private debts on U.S. output dynamics. We estimate a battery of Cointegrated Structural Vector Autoregressive models, and we identify structural shocks by employing Independent Component Analysis, a data-driven technique which avoids ad-hoc identification choices. The econometric results suggest that the impact of debt on […]

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working_paper_2016_20_cover

No Man Is an Island: The Impact of Heterogeneity and Local Interactions on Macroeconomic Dynamics

We develop an agent-based model in which heterogeneous firms and households interact in labor and good markets according to centralized or decentralized search and matching protocols. As the model has a deterministic backbone and a full-employment equilibrium, it can be directly compared to Dynamic Stochastic General Equilibrium (DSGE) models. We study the effects of negative […]

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working_paper_2016_13_cover

A Method for Agent-Based Models Validation

This paper proposes a new method for empirically validate simulation models that gen- erate artificial time series data comparable with real-world data. The approach is based on comparing structures of vector autoregression models which are estimated from both artificial and real-world data by means of causal search algorithms. This relatively simple procedure is able to […]

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