Dr%20Matteo%20Richiardi: List of publications

This paper estimates the causal relationship of supply of art on domestic tourist flows. To this aim, we use aggregate bilateral data on tourist flows and various data on museums in the twenty Italian regions. To solve the potential endogeneity of the supply of museums, we use three different empirical strategies: we use a fixed effects model controlling for bilateral macro-area dummies, we compute the degree of selection on unobservables relative to observables which would be necessary to drive the result to zero and, finally, we adopt a two-stage least squares approach that uses a measure of historical patronage, the number of noble families, as an instrument for the number of museums. For each empirical strategy, there is strong evidence of a positive effect of the number of ‘net-museums’ on bilateral tourist flows.

We introduce JAS-mine, a new Java-based computational platform that features tools to support the development of large-scale, data-driven, discrete-event simulations. JAS-mine is specifically designed for both agent-based and microsimulation modelling, anticipating a convergence between the two approaches. An embedded relational database management system provides tools for sophisticated input-output communications and data storage, allowing the power of relational databases to be used within an object-oriented framework. The JAS-mine philosophy encourages the separation of distinct concepts, objects and functionalities of the simulation model, and advocates and supports transparency, flexibility and modularity in model design. For instance, JAS-mine allows to store the list of regressors and their estimated coefficients externally to code, making it easy to change the specification of regression models used in the simulation and achieving a complete parallelisation between the tasks of the econometricians and those of the programmers. Moreover, tools for uncertainty analysis and search over the parameter space are also built in.

We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.

We provide a clarification of scaling issues in simulation models, distinguishing between sample size determination, discovery of emergent properties involving a qualitative change in the behaviour of the system at an aggregate level, and ‘true’ scaling, the dependence of the quantitative behaviour of the system at any given level of aggregation, to its size. Scaling issues arise because we want to understand what happens when we run one billion agents, without actually having to run one billion agents. We discuss how we can use the Buckingham Pi theorem, a key tool in dimensional analysis, to provide guidance on the nature and structure of scaling relationships in agent-based models. X-Classification-JEL: X-Keywords:

The economic crisis has revealed the dark side of deregulation in the labour market: rising unemployment, limited access to social security and, due to low wages, no savings to count upon in bad times. This book casts light on the empirical relationship between labour market deregulation through non-standard contracts and the three main dimensions of worker security: employment, income and social security. Focusing on individual work histories, it looks at how labour market dynamics interact with the social protection system in bringing about inequality and insecurity. In this context Italy is put forward as the epitome of flexibility through non-standard work and compared with three similar countries: Germany, Spain and Japan. Results show that when flexibility is carried out as a mere cost-reduction device and social security only relies on insurance principles, deregulation leads to insecurity. 'The political economy of work security and flexibility' is essential reading for academics, students, practitioners and policy makers interested in the outcomes of labour market developments in advanced economies over the past twenty years.

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations. © Copyright JASSS.

Multiplicative models of firm dynamics á la Gibrat have become a standard reference in industrial organization. However, some unpleasant properties of their implied dynamics - namely, their explosive or implosive behaviour (firm size and number collapsing to zero or increasing indefinitely) - have been given only very little attention. In this paper I investigate using simulations which modifications to the standard multiplicative model of firm dynamics lead to stable (and reasonable) distributions of firm size. I show that in order to obtain stable systems for a wide range of average growth rate, either heteroskedasticity in the growth rates has to be assumed, or entry and exit mechanisms included. In particular I show that combining the broad class of threshold entry mechanisms and the more restricted class of threshold exit mechanisms with overcapacity penalizing all firms (where entry and exit are determined with reference to an exogenously defined total capacity of the market), lead to stable distributions even in the case of growth rate homoskedasticity, given a non-zero minimum threshold for firm size.