Monetary Policy Design under Imperfect Knowledge: An Open Economy Analysis

Chen, Yu-Chin; Kulthanavit, Pisut

doi:10.2139/ssrn.1425906

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…Specifically, we take an agent-based version of the New Keynesian DSGE model. 3 In response to the recent criticisms (Colander et al, 2008;Colander, 2010;Solow, 2010;Velupillai, 2011;Stiglitz, 2011), some researchers have attempted to incorporate the three missing elements, i.e., bounded rationality, heterogeneity and interactions, into the DSGE models (Orphanides and Williams, 2007;Branch and McGough, 2009;Milani, 2009;Chen and Kulthanavit, 2010). This development leads to a kind of 'agentization' of the DSGE models, known as the agent-based DSGE models.…”

Section: The Approach and The Model Takenmentioning

confidence: 99%

Social Networks and Macroeconomic Stability

2014

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In this paper, the effect of the social network on macroeconomic stability is examined using an agent-based, network-based DSGE (dynamic stochastic general equilibrium) model. While the authors' primitive (first-stage) examination has the network generation mechanism as its main focus, their more in-depth second-stage analysis is based on a few main characteristics of network topologies, such as the degree, clustering coefficient, length, and centrality. Based on their econometric analysis of the simulation results, the authors find that the betweenness centrality contributes to the GDP instability and average path length contributes to the inflation instability. These results are robust under two augmentations, one taking into account nonlinearity and one taking into account the shape of the degree distribution as an additional characteristic. Through these augmentations, the authors find that the effect of network topologies on economic stability can be more intriguing than their baseline model may suggest: in addition to the existence of non-linear or combined effects of network characteristics, the shape of the degree distribution is also found to be significant. networks in this model are obviously indispensable, since they are the driving force behind the subsequent interactions of agents.Chen et al. (2014) used the well-known Ising model, invented by the physicist Ernst Ising in his PhD thesis in 1924, as a model for interacting agents with regard to their mimetic behavior. This Ising model is operated with different embedded network topologies. In this paper, we shall use the same model to address the significance of network topologies to macroeconomic stability. Let us be more precise in regard to what we try to do here. We shall simulate the macroeconomy using the agent-based DSGE model augmented with the Ising model, which is embedded with different network topologies. We shall then examine the effect of these different network topologies on the observed macroeconomic stability in terms of the output and inflation dynamics.As for the chosen network topologies, we consider two stages with different pursuits. In the first stage, we focus on network generation mechanisms, which include fully-connected networks, random networks, regular networks, small-world networks and scale-free networks. We then see whether there is any correspondence between macroeconomic stability and these generation mechanisms. The reason that we start with the network generation mechanism is because that earlier studies on agent-based economic models mainly focus on the network generation mechanisms (Albin and Foley, 1992;Wilhite, 2001). We should, however, realize that it is more informative to base our analysis upon network characteristics rather than network generation mechanisms. This is because the latter cannot uniquely define a network; two network generation mechanisms may happen to lead to the same network.There are many network characteristics; in this study, we restrict ourselves to five frequently used characteristics,...

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Section: The Approach and The Model Takenmentioning

confidence: 99%

Social Networks and Macroeconomic Stability

2014

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“…1 See Bask (200, Chang et al (2010), Wen (2010), Evans and Honkapohja (2001), Orphanides and Williams (2007a, b), Milani (2009), Branch and McGough (2009) and Chen and Kulthanavit (2010).…”

Section: _________________________mentioning

confidence: 99%

Interactions in the New Keynesian DSGE Models: The Boltzmann–Gibbs Machine and Social Networks Approach

Chen

Chang

2012

Economics

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The Boltzmann–Gibbs distribution is currently widely used in economic modeling. One of the applications is integrated with the DSGE (Dynamic Stochastic General Equilibrium) model. However, a question that arises concerns whether the Boltzmann–Gibbs distribution can be directly applied, without considering the underlying social network structure more seriously, even though the social network structure is an important factor of social interaction. Therefore, this paper proposes two kinds of agent-based DSGE models. The first one belongs to mesoscopic modeling in formulating the social interaction with the Boltzmann–Gibbs machine, and the other one belongs to microscopic modeling in that it is augmented by the network-based ant machine. By comparing the population dynamics generated by those different agent-based DSGE models, we find that the Boltzmann–Gibbs machine offers a good approximation of herding behavior. However, it is difficult to envisage the population dynamics produced by the Boltzmann–Gibbs machine and by the network-based ant machine as having the same distribution, particularly in popular empirical network structures such as small world networks and scale-free networks. Thus, the social interaction behavior may not be replaced by the Boltzmann–Gibbs distribution.

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“…Recently, Orphanides and Williams (2007a,b. ), Milani (2009), Branch and McGough (2009) and Chen and Kulthanavit (2010) have applied adaptive learning mechanisms to some new Keynesian DSGE models.…”

Section: Introductionmentioning

confidence: 99%

“…During the last decade, the Boltzmann-Gibbs distribution has been widely used for modeling financial markets. A detailed survey of the use of the Boltzmann-Gibbs approach can be found in Chen et al (2010). In general, the Boltzmann-Gibbs distribution is often used to deal with expectation behavior; it can, therefore, be applied to models incorporating expectations, such as the cobweb model, asset pricing model and positive versus negative feedback model, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The ant model endogenously creates swings and herding behavior in aggregate expectations through interaction and has successfully replicated stylized facts of financial markets (Chen et al, 2010). Therefore, this paper proposes a network-based ant model and tries to compare the population dynamics in the Boltzmann-Gibbs model with different network structure models which are applied to stylized New Keynesian DSGE models.…”

Section: Introductionmentioning

confidence: 99%

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Interactions in DSGE Models: The Boltzmann-Gibbs Machine and Social Networks Approach

Chang¹,

Chen²

2011

SSRN Journal

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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. Abstract While DSGE models have been widely used by central banks for policy analysis, they seem to have been ineffective in calibrating the models for anticipating financial crises. To bring DSGE models closer to real situations, some of researchers have revised the traditional DSGE models. One of the modified DSGE models is the adaptive belief system model. In this framework, changes in sentiment can be expounded by a Boltzmann-Gibbs distribution, and in addition to externally caused fluctuations endogenous interactions are also considered. Methodologically, heuristic switching models are mesoscopic. For this reason, the social network structure is not described in the adaptive belief system models, even though the network structure is an important factor of interaction. The interaction behavior should ideally be based on some kind of social network structures. Today, the Boltzmann-Gibbs distribution is widely used in economic modeling. However, the question is whether the Boltzmann-Gibbs distribution can be directly applied, without considering the underlying social network structure more seriously. To this day, it seems that few scholars have discussed the relationship between social networks and the Boltzmann-Gibbs distribution. Therefore, this paper proposes a network based ant model and tries to compare the population dynamics in the Boltzmann-Gibbs model with different network structure models applied to stylized DSGE models. We find that both the Boltzmann-Gibbs model and the network-based ant model could generate herding behavior. However, it is difficult to envisage the population dynamics generated by the Boltzmann-Gibbs model and the network-based ant model having the same distribution, particularly in popular empirical network structures such as small world networks and scale-free networks. In addition, our simulation results further suggest that the population dynamics of the Boltzmann-Gibbs model and the circle network ant model can be considered with the same distribution under specific parameters settings. This finding is consistent with the study of thermodynamics, on which the Boltzmann-Gibbs distribution is based, namely, the local interaction. Terms of use: Documents inPaper submitted to the special issueNew Approaches in Quantitative Modeling of Financial Markets JEL C63, D85, E12, E32, E37

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Monetary Policy Design under Imperfect Knowledge: An Open Economy Analysis

Cited by 5 publications

References 33 publications

Social Networks and Macroeconomic Stability

Social Networks and Macroeconomic Stability

Interactions in the New Keynesian DSGE Models: The Boltzmann–Gibbs Machine and Social Networks Approach

Interactions in DSGE Models: The Boltzmann-Gibbs Machine and Social Networks Approach

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