Alessio Moneta and Elena Stepanova

Author Archive | Alessio Moneta and Elena Stepanova


Innovation, demand and growth

The attached paper, titled “Changes in consumption patterns and innovation: an empirical analysis” was written with the aim of addressing the empirical part of the Task 6.1 of ISIGrowth, which is devoted to study the interactions between innovation, demand generation and aggregate growth. The general goal of Task 6.1 is to investigate the conditions under […]

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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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Dynamic Increasing Returns and Innovation Diffusion: bringing Polya Urn processes to the empirical data

The patterns of innovation diffusion are well approximated by the logistic curves. This is the robust empirical fact confirmed by many studies in innovations dynamics. Here we show that the logistic pattern of innovation diffusion can be replicated by the time-dependent stochastic process with positive feedbacks along the diffusion trajectory. The dynamic increasing returns process […]

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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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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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