The speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a confirmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be influenced by peers with relatively different preferences, reducing the overall diffusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diffusion for different parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption.
2019 / Hibbah, E.H., H. El Maroufy, C. Fuchs and T. Ziad
An MCMC computational approach for a continuous time state-dependent regime switching diffusion process
JOURNAL OF APPLIED STATISTICS, Vol. 47, Issue 8, 1354-1374
State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.
2019 / Smrkolj, G. and F. Wagener
Research among copycats: R&D, spillovers, and feedback strategies
International Journal of Industrial Organization, Vol. 65, 82-120
We study a stochastic dynamic game of process innovation in which firms can initiate and terminate R&D efforts and production at different times. We discern the impact of knowledge spillovers on the investments in existing markets, as well as on the likely structure of newly forming markets, for all possible asymmetries in production costs between firms. While an increase in spillovers may improve the likelihood of a competitive market, it may at the same time reduce the level to which a technology is developed. We show that the effects of spillovers on investments and surpluses crucially depend on the stage of technology development considered. In particular, we show that high spillovers are not necessarily pro-competitive as they can make it harder for the laggard to catch up with the technology leader.
2019 / Cornea-Madeira, A., C. Hommes and D. Massaro
Behavioral Heterogeneity in U.S. Inflation Dynamics
Journal of Business & Economic Statistics, Vol. 37(2), 288-300
In this article we develop and estimate a behavioral model of inflation dynamics with heterogeneous firms. In our stylized framework there are two groups of price setters, fundamentalists and random walk believers. Fundamentalists are forward-looking in the sense that they believe in a present-value relationship between inflation and real marginal costs, while random walk believers are backward-looking, using the simplest rule of thumb, naive expectations, to forecast inflation. Agents are allowed to switch between these different forecasting strategies conditional on their recent relative forecasting performance. We estimate the switching model using aggregate and survey data. Our results support behavioral heterogeneity and the significance of evolutionary learning mechanism. We show that there is substantial time variation in the weights of forward-looking and backward-looking behavior. Although on average the majority of firms use the simple backward-looking rule, the market has phases in which it is dominated by either the fundamentalists or the random walk believers.
2019 / Liuzzi, D., P. Pellizzari and M. Tolotti
Fast traders and slow price adjustments: an artificial market with strategic interaction and transaction costs
Journal of Economic Interaction and Coordination, Vol. 14, 643–662
In this paper, we propose an artificial market to model high-frequency trading where fast traders use threshold rules strategically to issue orders based on a signal reflecting the level of stochastic liquidity prevailing on the market. A market maker is in charge of adjusting prices (on a fast scale) and of setting closing prices and transaction costs on a daily basis, controlling for the volatility of returns and market activity. We first show that a baseline version of the model with no frictions is able to generate returns endowed with several stylized facts. This achievement suggests that the two time scales used in the model are one (possibly novel) way to obtain realistic market outcomes and that high-frequency trading can amplify liquidity shocks. We then explore whether transaction costs can be used to control excess volatility and improve market quality. While properly implemented taxation schemes may help in reducing volatility, care is needed to avoid excessively curbing activity in the market and intensifying the occurrence of abnormal peaks in returns.
2019 / Hommes, C. and J. Lustenhouwer
Inflation targeting and liquidity traps under endogenous credibility
Policy implications are derived for an inflation-targeting central bank, whose credibility is endogenous and depends on its past ability to achieve its targets. This is done in a New Keynesian framework with heterogeneous and boundedly rational expectations. We find that the region of allowed policy parameters is strictly larger than under rational expectations. However, when the zero lower bound on the nominal interest rate is accounted for, self-fulfilling deflationary spirals can occur, depending on the credibility of the central bank. Deflationary spirals can be prevented with a high inflation target and aggressive monetary easing.
2019 / El Omari M., H. El Maroufy and C. Fuchs
Non parametric estimation for fractional diffusion processes with random effects
We propose a nonparametric estimation for a class of fractional stochastic differential equations (FSDE) with random effects. We precisely consider general linear fractional stochastic differential equations with drift depending on random effects and non-random diffusion. We build ordinary kernel estimators and histogram estimators and study their risk (), when . Asymptotic results are evaluated as both and N tend to infinity.
2019 / Dawid H., P. Harting and S. van der Hoog
Manager Remuneration, Share Buybacks and Firm Performance
Using a dynamic heterogeneous agent industry model, we examine the impact of manager remuneration schemes on firms’ investment decisions and on the evolution of their competitiveness and share values. Whereas an increase in the share-based manager remuneration component is always beneficial to the manager, it is beneficial for shareholders only if such a change in the remuneration scheme is adopted by all firms in the industry. In that case, productivity growth is slowed down and workers’ real wages are reduced.
2019 / Assenza, T and D. Delli Gatti
The financial transmission of shocks in a simple hybrid macroeconomic agent based model
Journal of Evolutionary Economics, Vol. 29, 265–297
Tracking the chain of events generated by an aggregate shock in an Agent Based Model (ABM) is apparently an impossible mission. Employing the methodology described in Assenza and Delli Gatti (J Econ Dyn Control 37(8):1659–1682 2013) (AD2013 hereafter), in the present paper we show that such a task can be carried out in a straightforward way by using a hybrid macro ABM consisting of a IS curve, an Aggregate Supply (AS) curve and a Taylor Rule (TR) in that aggregate investment is a function of the moments of the distribution of firms’ net worth. For each shock (fiscal expansion, monetary tightening, financial shock) we can decompose the change of the aggregate scale of activity (measured by the employment rate) in a first round effect – i.e., the change generated by the shock keeping the moments of the distribution of net worth at the pre-shock level – and a second round effect, i.e., the change brought about by the variation in the moments induced by the aggregate shock. In turn, the second round effect can be decomposed in a term that would show up also in a pure Representative Agent setting (RA component) and a term that is specific to the model with Heterogeneous Agents (HA component). In all the cases considered, the first round effect explains most of the actual change of the output gap. The second round effect is unambiguously negative. The HA component has the same sign of the RA component and explains a sizable fraction of the second round effect.
2019 / van der Hoog, S.
Surrogate Modelling in (and of) Agent-Based Models: A Prospectus
Computational Economics, Springer, Society for Computational Economics, Vol. 53(3), 1245-1263
A very timely issue for economic agent-based models (ABMs) is their empirical estimation. This paper describes a line of research that could resolve the issue by using machine learning techniques, using multi-layer artificial neural networks (ANNs), or so called Deep Nets. The seminal contribution by Hinton et al. (Neural Comput 18(7):1527–1554, 2006) introduced a fast and efficient training algorithm called Deep Learning, and there have been major breakthroughs in machine learning ever since. Economics has not yet benefited from these developments, and therefore we believe that now is the right time to apply multi-layered ANNs and Deep Learning to ABMs in economics.
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