Estimation of ergodic agent-based models by simulated minimum distance

Grazzini, Jakob; Richiardi, Matteo

doi:10.1016/j.jedc.2014.10.006

Cited by 115 publications

(74 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though Agent Based Models (ABMs) have often been advocated as promising alternatives to neoclassical models rooted in the dogmatic paradigms of rational expectations and representative agents, there are still some concerns about how to bring them down to the data (Windrum et al, 2007;Gallegati and Richiardi, 2009;Grazzini and Richiardi, 2015). In macroeconomics, for example, Giannone et al (2006), Canova and Sala (2009) and Paccagnini (2009) provide details about how to estimate and validate Dynamic Stochastic General Equilibrium models.…”

Section: Introductionmentioning

confidence: 99%

“…Starting from the same procedure, Gilli and Winker (2003) and Winker et al (2007) introduced an algorithm and a set of statistics leading to the construction of an objective function used to estimate models of exchange rate and to push them closer to the properties of real data. 1 Recently, Grazzini and Richiardi (2015) proposed the approach of simulated minimum distance for estimation of ergodic ABMs, both in the long run equilibrium and during transitional dynamics, while Recchioni et al (2015) used a simple gradient-based calibration procedure to conveniently sample the parameter space minimizing a standard loss function based on the cumulative squared errors. The key choice of a calibration exercise seems to boil down to the function that measures models' fit 1 On the use of indirect inference see also Fabretti (2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical validation of simulated models through the GSL-div: an illustrative application

Lamperti

2017

J Econ Interact Coord

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractA 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 new criterion, called GSL-div and developed in Lamperti (2015), to assess the degree of similarity between the dynamics observed in the data and those generated by the numerical simulation of models. As an illustrative application, this approach is used to distinguish between different versions of the well known asset pricing model with heterogeneous beliefs proposed in Brock and Hommes (1998). Once the discrimination ability of the GSL-div is proved, model's dynamics are directly compared with actual data coming from two major stock market indexes (EuroSTOXX 50 for Europe and CSI 300 for China).Results show that the model, once calibrated, is fairly able to track the evolution of both the two indexes, even though a better fit is reported for the Chinese stock market. However, I also find that many different combinations of traders behavioural rules are compatible with the same observed dynamics. Within this heterogeneity, an emerging common trait is found: to be empirically valid, the model has to account for a strong trend following component, which might either come from a unique trend type that heavily extrapolates information from past observations or the combinations of different types with milder, or even opposite, attitudes towards the trend.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical validation of simulated models through the GSL-div: an illustrative application

Lamperti

2017

J Econ Interact Coord

View full text Add to dashboard Cite

show abstract

“…Our viewpoint of empirically determining all measurable parameters and calibrating the model by choosing the non-measurable behavioural parameters, completed by a thorough sensitivity analysis, is certainly not the only option. It is advocated by Cirillo and Gallegati (2012) but criticized by Grazzini and Richiardi (2015), who argue in favour of estimating all parameters through simulation. Yet another viewpoint is that of estimating the parameters in a Bayesian way, making use of all available qualitative and quantitative information to go from distributions of input values and arrive at distributions of output values (Poole and Raftery 2000;Bijak et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Multistate modelling extended by behavioural rules: An application to migration

et al. 2017

View full text Add to dashboard Cite

We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age-and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.Keywords: international migration; life course modelling; Theory of Planned Behaviour; multistate modelling; microsimulation; agent-based modelling IntroductionFor policymakers and researchers alike, it is of paramount interest to be able to predict how people make demographically relevant decisions. In this paper, we present a novel approach to projection by incorporating decision-making and interaction between individuals in a demographic projection model. We use an individual-based model rather than a population-based model such as the popular cohort-component model. For an overview of approaches to forecasting migration, see Bijak (2011), as well as other recent contributions from Hatton and Williamson (2011), Azose and Raftery (2013), and Abel and Sander (2014. This individual-based micro perspective enables us to incorporate behavioural mechanisms and social processes that influence demographic behaviour and population change into our model. We can thus predict the effect of external shocks, such as policy changes, by making the causal mechanism through which shocks alter behaviour explicit. Comparing model predictions with empirical outcomes facilitates the subsequent drawing of conclusions on the plausibility of the assumed behavioural mechanism. In this way, the model can be improved gradually over time, by refining the parameters and functional forms of its main component, in this case the decision-making on international migration.In the model, as in real life, demographically relevant decisions are embedded in the human life course. Choices are motivated by aspirations and preferences in different domains of life, such as work and family, and constrained by available resources (not only financial, but also cognitive and social resources, and time). We take into consideration that people do not have perfect foresight and usually lack the time and cognitive abilities to acquire the full and unbiased information necessary to make a rational choice. Preferences vary over the life course, as do both the availability of and need for resources, because of events or changing conditions (e.g., available social support). As a result, t...

show abstract

“…Unfortunately, recent developments in agent-based macro-economics have led to the development of more and more complex models, which require large sets of parameters to adequately capture the complexity of micro-founded, multi-sector and possibly multi-country phenomena 6 See also Grazzini and Richiardi (2015) and Fabretti (2012) for other applications of the same approach (see Fagiolo and Roventini, 2017, for a recent survey). In such a setting, neither direct estimations nor global sensitivity analysis (often advocated as a natural approach to ABM exploration, cf.…”

Section: Calibration and Validation Of Agent-based Models: The Case Fmentioning

confidence: 99%

Agent-Based Model Calibration Using Machine Learning Surrogates

2017

View full text Add to dashboard Cite

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 accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large outof-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces. KEY WORDSAgent based model; calibration; machine learning; surrogate; meta-model. JELC15, C52, C63.

show abstract

Estimation of ergodic agent-based models by simulated minimum distance

Cited by 115 publications

References 87 publications

Empirical validation of simulated models through the GSL-div: an illustrative application

Empirical validation of simulated models through the GSL-div: an illustrative application

Multistate modelling extended by behavioural rules: An application to migration

Agent-Based Model Calibration Using Machine Learning Surrogates

Contact Info

Product

Resources

About