This note discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to reestimate unknown parameters on each simulated Monte-Carlo sample - and...

This work brings together two distinct pieces of evidence concerning, at the macro level, international distributions of incomes and their dynamics, and, at the micro level, the size distributions of firms and the properties of their growth rates. First, our empirical analysis provides a new...

This work brings together two distinct pieces of evidence concerning, at macro level, international distributions of incomes and their dynamics, and, at micro level, the size distributions of firms and the properties of their growth rates. Moreover, we take into the picture an intermediate level...

Simulated models suffer intrinsically from validation and comparison problems. The choice of a suitable indicator quantifying the distance between the model and the data is pivotal to model selection. However, how to validate and discriminate between alternative models is still an open problem...

We propose a novel approach to the statistical analysis of simulation models and, especially, agent-based models (ABMs). Our main goal is to provide a fully automated and model-independent tool-kit to inspect simulations and perform counter-factual analysis. Our approach: (i) is easy-to-use by...

Firms grow and decline by relatively lumpy jumps which cannot be accounted by the cumulation of small, "atom-less", independent shocks. Rather "big" episodes of expansion and contraction are relatively frequent. More technically, this is revealed by fat tail distributions of growth rates. This...

A major concern about the use of simulation models regards their relationship with the empirical data. The identification of a suitable indicator quantifying the distance between the model and the data would help and guide model selection and output validation. This paper proposes the use of a...

Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and...

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...

Simar and Wilson (J. Econometrics, 2007) provided a statistical model that can rationalize two-stage estimation of technical efficiency in nonparametric settings. Two-stage estimation has been widely used, but requires a strong assumption: the second-stage environmental variables cannot affect...