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題名 社會網路互動下的新凱因斯動態隨機一般均衡模型
Toward a social network-based New Keynesian DSGE model
作者 張嘉玲
Chang, Chia Ling
貢獻者 陳樹衡
Chen, Shu Heng
張嘉玲
Chang, Chia Ling
關鍵詞 社會網路互動下的新凱因斯動態隨機一般均衡模型
效用基礎下波茲曼分配
投資儲蓄迷思
加總問題
Social Network-Based New Keynesian DSGE Model
Performance-Based Boltzmann-Gibbs Distribution
IS Puzzle
Aggregation Problem
日期 2010
上傳時間 5-Sep-2013 14:17:45 (UTC+8)
摘要 本研究建構一社會網路互動下的新凱因斯動態隨機一般均衡模型，探討效用基礎下波茲曼分配背後的網路結構，以及，社會網路對新凱因斯動態隨機一般均衡模型參數的影響。根據本論文模擬結果，效用基礎下波茲曼分配背後所隱含的社會網路結構呈現局部區域性連結拓璞，此結論與熱力學對波茲曼分配中粒子互動方式的假設相同，然而，區域性連結之網路結構（如環狀網）並非目前實證研究所觀察到的網路型態（如冪分布網路或高群集係數之小世界網路），故吾人是否得以直接利用效用基礎下波茲曼分配來描述社會上人與人之間的互動現象必需更忱慎考量之。另外，社會網路互動也將使新凱因斯動態隨機一般均衡模型之參數估計產生偏誤，依本研究估計結果觀之，只要加入社會互動，總合需求曲線中實質利率之參數估計將為正號，即實質利率對產出缺口的影響為負向影響，也就是文獻上的投資儲蓄迷思(IS puzzle)，若進一步觀察社會網路結構對該實證迷思的影響則可發現當社會網路群聚程度越高時，該估計偏誤將越嚴重。
We construct a social network-based New Keynesian DSGE (Dynamic Stochastic General Equilibrium) Model to investigate the underlying social network structure derived from the performance-based Boltzmann-Gibbs model, and thus interpret the process that social network structures affect the estimation bias in the New Keynesian DSGE framework. According to our simulation results, the underlying social network structure derived from the performance-based Boltzmann-Gibbs model should be local. This finding is consistent with the study of thermodynamics, which the Boltzmann-Gibbs distribution is based upon, i.e. the local interaction. However, it contradicts not only the purpose of combining the performance-based Boltzmann-Gibbs machine and New Keynesian DSGE model, but also empirical studies of social network structures in the real world. Accordingly, maybe we have to consider further whether the performance-based Boltzmann-Gibbs machine is a suitable tool for calibrating social interaction under the stylized New Keynesian DSGE framework. Furthermore, if we embedded interaction behavior in the stylized New Keynesian model, the so-called “IS Puzzle” can be consequently observed. We also realized that “IS Puzzle” is connected with network structures. The more clustering the network structure is, the more significant “IS Puzzle” would be.
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Press, NJ.

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Aiello,W., F. Chung, and L. Lu (2002), Random evolution of massive graphs,
In: Handbook of Massive Data Sets, Abello J., Pardalos P. M., and
Resende M. G. C. (Eds.), Kluwer Press, Dordrecht.

Albert, R., H. Jeong, and A.-L. Barabási (1999), Diameter of the world-wide
Web, Nature, 401:130–131.

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Alfarano S., M. Milakovic M. and M. Raddant (2009), Network hierarchy in Kirman’s ant model: fund investment can create systemic risk, Working Paper.

Anderson, P.W. (1972), More is different, Science, 177: 393-396.

Assenza, T., P. Heemeijer, C. Hommes, and D. Massaro (2009), Experimenting with expectations: From individual behavior in the Lab to aggregate macro behavior, Working Paper.

Barabási, A.-L. and R. Albert (1999), Emergence of scaling in random networks,
Science, 286:509–512.

Bask, M. (2007), Long swings and chaos in the exchange rate in a DSGE model with a Taylor rule, Working Paper.

Bask, M. (2009), Monetary policy, stock price misalignments and macroeconomic instability, Working Paper.

Blume, L. (1993), The statistical mechanics of strategic interaction, Games and Economic Behavior, 5: 387-424.

Boltzmann, L. (1872), Weitere studien uber das warmegleichgewichtunter gasmolekulen, Wiener Berichte , 66:275–370.

Branch, W.A. and B. McGough (2009), A new Keynesian model with heterogeneous expectations, Journal of Economic Dynamics and Control, 33:1036–1051.

Brock, W. and C. Hommes (1997), A rational route to randomness, Econometrica, 65:1059-1095.

Brock, W. and C. Hommes (1998), Heterogeneous beliefs and routes to chaos in a simple asset pricing model, Journal of Economic Dynamics and Control, 22: 1235-1274.

Chang, Y., S. B. Kim, and F. Schorfheide (2010), Financial fricitions, aggregation, and the Lucas Critique, Working Paper.

Chen, S.H., C.-L. Chang, and Y.-R. Du (2010), Agent-based economic models and econometrics, Knowledge Engineering Review, forthcoming.

Chen, Y. C. and P. Kulthanavit (2010), Monetary policy design under imperfect knowledge: An open economy analysis, Working Paper.

Colander, D. (2006), Post Walrasian Macro: Beyond the DSGE Model, Cambridge University Press, Cambridge.

Cont, R. and J. P. Bouchaud (2000), Herd behaviour and aggregate fluctuations in financial markets, Macroeconomic Dynamics, 4:170–196.

Deaton, A. (1992), Understanding Consumption, Oxford University Press, NY.

De Grauwe, P. (2010a), The scientific foundation of dynamic stochastic general equilibrium (DSGE) models, Public Choice, 144:413-443.


De Grauwe, P. (2010b), Animal spirits and monetary policy, Economic Theory, online first.

Driffill, J. (2008), Macroeconomic theory and the global economic crises, Mimeo, Birkbeck College.
Ebel, H., L.-I. Mielsch, and S. Bornholdt (2002), Scale-free topology of e-mail networks, Physical Review E, 66:035103.

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Faloutsos, M., P. Faloutsos, and C. Faloutsos (1999), On power-law relationships of the internet topology, Computer Communications Review, 29:251-262.

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Hildenbrand W. and A. Kneip (2005), Aggregate behavior and micro data, Games and Economic Behavior, 50:3-27.

Howitt, P., A. Kirman, A. Leijonhufvud, P. Mehrling and D. Colander (2008), Beyond DSGE models: toward an empirically based macroeconomics, American Economic Review, 98:236-240.

Iori, G. (2002), A micro-simulation of traders’ activity in the stock market: the role of heterogeneity, agents’ interactions and trade friction, Journal of Economic Behavior and Organization, 49:269–285.

Iori, G., G. De Masi, O. Precup, G. Gabbi, and G. Caldarelli (2008), A network analysis of the Italian Overnight Money Market, Journal of Economic Dynamics and Control, 32:259-278.

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描述 博士
國立政治大學
經濟學系
93258508
99
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0093258508
資料類型 thesis
dc.contributor.advisor 陳樹衡zh_TW
dc.contributor.advisor Chen, Shu Hengen_US
dc.contributor.author (Authors) 張嘉玲zh_TW
dc.contributor.author (Authors) Chang, Chia Lingen_US
dc.creator (作者) 張嘉玲zh_TW
dc.creator (作者) Chang, Chia Lingen_US
dc.date (日期) 2010en_US
dc.date.accessioned 5-Sep-2013 14:17:45 (UTC+8)-
dc.date.available 5-Sep-2013 14:17:45 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2013 14:17:45 (UTC+8)-
dc.identifier (Other Identifiers) G0093258508en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60322-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 93258508zh_TW
dc.description (描述) 99zh_TW
dc.description.abstract (摘要) 本研究建構一社會網路互動下的新凱因斯動態隨機一般均衡模型，探討效用基礎下波茲曼分配背後的網路結構，以及，社會網路對新凱因斯動態隨機一般均衡模型參數的影響。根據本論文模擬結果，效用基礎下波茲曼分配背後所隱含的社會網路結構呈現局部區域性連結拓璞，此結論與熱力學對波茲曼分配中粒子互動方式的假設相同，然而，區域性連結之網路結構（如環狀網）並非目前實證研究所觀察到的網路型態（如冪分布網路或高群集係數之小世界網路），故吾人是否得以直接利用效用基礎下波茲曼分配來描述社會上人與人之間的互動現象必需更忱慎考量之。另外，社會網路互動也將使新凱因斯動態隨機一般均衡模型之參數估計產生偏誤，依本研究估計結果觀之，只要加入社會互動，總合需求曲線中實質利率之參數估計將為正號，即實質利率對產出缺口的影響為負向影響，也就是文獻上的投資儲蓄迷思(IS puzzle)，若進一步觀察社會網路結構對該實證迷思的影響則可發現當社會網路群聚程度越高時，該估計偏誤將越嚴重。zh_TW
dc.description.abstract (摘要) We construct a social network-based New Keynesian DSGE (Dynamic Stochastic General Equilibrium) Model to investigate the underlying social network structure derived from the performance-based Boltzmann-Gibbs model, and thus interpret the process that social network structures affect the estimation bias in the New Keynesian DSGE framework. According to our simulation results, the underlying social network structure derived from the performance-based Boltzmann-Gibbs model should be local. This finding is consistent with the study of thermodynamics, which the Boltzmann-Gibbs distribution is based upon, i.e. the local interaction. However, it contradicts not only the purpose of combining the performance-based Boltzmann-Gibbs machine and New Keynesian DSGE model, but also empirical studies of social network structures in the real world. Accordingly, maybe we have to consider further whether the performance-based Boltzmann-Gibbs machine is a suitable tool for calibrating social interaction under the stylized New Keynesian DSGE framework. Furthermore, if we embedded interaction behavior in the stylized New Keynesian model, the so-called “IS Puzzle” can be consequently observed. We also realized that “IS Puzzle” is connected with network structures. The more clustering the network structure is, the more significant “IS Puzzle” would be.en_US
dc.description.tableofcontents 1 Introduction 1

2 The Model 6

2.1 The stylized New Keynesian DSGE model 6
2.2 The Performance-based Boltzmann-Gibbs machine :
Adaptive belief system 9
2.3 The social network structure 13
2.3.1 Generating algorithms of different social network structures 13
2.3.2 The statistics of social network structure 21
2.4 The Ising model 23
2.5 The network-based ant model 24

3 Experimental Designs 27

4 The Underlying Social Network Structure of the Performance-based Boltzmann-Gibbs model 31
4.1 Motivation 33
4.2 Experimental results of the Ising model 37
4.3 Experimental results of the network-based ant model 44
4.4 Summary 48

5 Aggregation Problem in the New Keynesian DSGE Model 50
5.1 Motivation 51
5.2 The Estimation bias of the New Keynesian DSGE model 56
5.2.1 Correlation coefficient analysis 62
5.2.2 Threshold regression analysis 64
5.3 Summary 67

6 Conclusion and Future Research 69

Reference 73
Appendixes 79



List of Tables



2.1 Basic statistics of different social networks 22
3.1 Parameters setting of the stylized New Keynesian DSGE model 27
3.2 Parameter setting of the performance-based Boltzmann-Gibbs model
and Ising model 28
3.3 Parameters setting of network-based ant model 29
3.4 Parameters setting of different network structure 29
3.5 Other parameters 30
4.1 The p-value of Kolmogorov-Smirnov statistic 39
4.2 The results of relative entropy 41
4.3 The shape of the optimistic ratios’ probability density function with
different values of the intensity of choice 42
4.4 Number the of best candidate for the underlying network structure of the
performance-based Boltzmann-Gibbs machine 44
4.5 Kolmogorov-Smirnov test results (N=100) 46
4.6 Kolmogorov-Smirnov test results (N=1000) 46
4.7 Relative entropy 47
5.1 Estimation bias of the New Keynesian DSGE model 59
5.2 Correlation coefficient 64
5.3 Results of threshold regression 66














List of Figures



2.1 Fully-connected network 14
2.2 Circle network 15
2.3 Regular network 15
2.4 Small world network with rewiring rate 0.1 17
2.5 Small world network with rewiring rate 0.3 17
2.6 Small world network with rewiring rate 0.5 18
2.7 Small world network with rewiring rate 0.7 18
2.8 Small world network with rewiring rate 0.9 19
2.9 Random network 19
2.10 Scale free network 20























List of Appendixes


A.1 Probability density function of optimistic ratios
with intensity of choice (λ) = 100 79
A.2 Probability density function of optimistic ratios
with intensity of choice (λ) = 500 83
A.3 Probability density function of optimistic ratios
with intensity of choice (λ) = 1000 87
A.4 Probability density function of optimistic ratios
with intensity of choice (λ) = 5000 91
A.5 Probability density function of optimistic ratios
with intensity of choice (λ) = 10000 95
A.6 Probability density function of optimistic ratios
with intensity of choice (λ) = 50000 99
A.7 Probability density function of optimistic ratio of
network based ant model (N=100) 103
A.8 Probability density function of optimistic ratio of
network based ant model (N=1000) 106		zh_TW
dc.format.extent 290599 bytes-
dc.format.extent 211475 bytes-
dc.format.extent 2323758 bytes-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0093258508en_US
dc.subject (關鍵詞) 社會網路互動下的新凱因斯動態隨機一般均衡模型zh_TW
dc.subject (關鍵詞) 效用基礎下波茲曼分配zh_TW
dc.subject (關鍵詞) 投資儲蓄迷思zh_TW
dc.subject (關鍵詞) 加總問題zh_TW
dc.subject (關鍵詞) Social Network-Based New Keynesian DSGE Modelen_US
dc.subject (關鍵詞) Performance-Based Boltzmann-Gibbs Distributionen_US
dc.subject (關鍵詞) IS Puzzleen_US
dc.subject (關鍵詞) Aggregation Problemen_US
dc.title (題名) 社會網路互動下的新凱因斯動態隨機一般均衡模型zh_TW
dc.title (題名) Toward a social network-based New Keynesian DSGE modelen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Akerlof, G.A. and R. J. Shiller (2009), Animal Spirits, Princeton University
Press, NJ.

Kalejan, H. H. (1980), Aggregation and disaggregation of nonlinear equations, In: Evaluation of Econometrics Models, Kmenta J. and Ramsey J. B. (Eds.), Academic Press, NY.

Aiello,W., F. Chung, and L. Lu (2002), Random evolution of massive graphs,
In: Handbook of Massive Data Sets, Abello J., Pardalos P. M., and
Resende M. G. C. (Eds.), Kluwer Press, Dordrecht.

Albert, R., H. Jeong, and A.-L. Barabási (1999), Diameter of the world-wide
Web, Nature, 401:130–131.

Alfarano, S. and M. Milakovic (2007), Should network structure matter in agent-based finance?, Working Paper.

Alfarano S., M. Milakovic M. and M. Raddant (2009), Network hierarchy in Kirman’s ant model: fund investment can create systemic risk, Working Paper.

Anderson, P.W. (1972), More is different, Science, 177: 393-396.

Assenza, T., P. Heemeijer, C. Hommes, and D. Massaro (2009), Experimenting with expectations: From individual behavior in the Lab to aggregate macro behavior, Working Paper.

Barabási, A.-L. and R. Albert (1999), Emergence of scaling in random networks,
Science, 286:509–512.

Bask, M. (2007), Long swings and chaos in the exchange rate in a DSGE model with a Taylor rule, Working Paper.

Bask, M. (2009), Monetary policy, stock price misalignments and macroeconomic instability, Working Paper.

Blume, L. (1993), The statistical mechanics of strategic interaction, Games and Economic Behavior, 5: 387-424.

Boltzmann, L. (1872), Weitere studien uber das warmegleichgewichtunter gasmolekulen, Wiener Berichte , 66:275–370.

Branch, W.A. and B. McGough (2009), A new Keynesian model with heterogeneous expectations, Journal of Economic Dynamics and Control, 33:1036–1051.

Brock, W. and C. Hommes (1997), A rational route to randomness, Econometrica, 65:1059-1095.

Brock, W. and C. Hommes (1998), Heterogeneous beliefs and routes to chaos in a simple asset pricing model, Journal of Economic Dynamics and Control, 22: 1235-1274.

Chang, Y., S. B. Kim, and F. Schorfheide (2010), Financial fricitions, aggregation, and the Lucas Critique, Working Paper.

Chen, S.H., C.-L. Chang, and Y.-R. Du (2010), Agent-based economic models and econometrics, Knowledge Engineering Review, forthcoming.

Chen, Y. C. and P. Kulthanavit (2010), Monetary policy design under imperfect knowledge: An open economy analysis, Working Paper.

Colander, D. (2006), Post Walrasian Macro: Beyond the DSGE Model, Cambridge University Press, Cambridge.

Cont, R. and J. P. Bouchaud (2000), Herd behaviour and aggregate fluctuations in financial markets, Macroeconomic Dynamics, 4:170–196.

Deaton, A. (1992), Understanding Consumption, Oxford University Press, NY.

De Grauwe, P. (2010a), The scientific foundation of dynamic stochastic general equilibrium (DSGE) models, Public Choice, 144:413-443.


De Grauwe, P. (2010b), Animal spirits and monetary policy, Economic Theory, online first.

Driffill, J. (2008), Macroeconomic theory and the global economic crises, Mimeo, Birkbeck College.
Ebel, H., L.-I. Mielsch, and S. Bornholdt (2002), Scale-free topology of e-mail networks, Physical Review E, 66:035103.

Evans, G. and S. Honkapohja (2001), Learning and Expectations in Macroeconomics, Princeton University Press, Princeton, NJ.

Faloutsos, M., P. Faloutsos, and C. Faloutsos (1999), On power-law relationships of the internet topology, Computer Communications Review, 29:251-262.

Forni, M. and M. Lippi (1997), Aggregation and the Microfoundations of Dynamic Macroeconomics, Clarendon Press, Oxford.

Gallegati, M., A. Palestrini, D. Delli Gatti and E. Scalas (2006), Aggregation of heterogeneous interacting agents: the variant representative agent framework, Journal of Economic Interaction and Coordination,1: 5-19.

Goodhart, C. and B. Hofmann (2005), The IS curve and the transmission of monetary policy: is there a puzzle?, Applied Economics, 37(1): 29-36.

Granger, C.W.J. (1980), Long memory relationships and the aggregation of dynamic models, Journal of Econometrics,14:227–238.

Hansen, B. E. (2000), Sample Splitting and Threshold Estimation, Econometrica, 68:575-604.

Hildenbrand W. and A. Kneip (2005), Aggregate behavior and micro data, Games and Economic Behavior, 50:3-27.

Howitt, P., A. Kirman, A. Leijonhufvud, P. Mehrling and D. Colander (2008), Beyond DSGE models: toward an empirically based macroeconomics, American Economic Review, 98:236-240.

Iori, G. (2002), A micro-simulation of traders’ activity in the stock market: the role of heterogeneity, agents’ interactions and trade friction, Journal of Economic Behavior and Organization, 49:269–285.

Iori, G., G. De Masi, O. Precup, G. Gabbi, and G. Caldarelli (2008), A network analysis of the Italian Overnight Money Market, Journal of Economic Dynamics and Control, 32:259-278.

Jackson, M.O. (2005), A survey of models of network formation: stability and efficiency, In: Group Formation in Economics: Networks, Clubs and Coalitions, G. Demange G. and Wooders M. (Eds.), Cambridge University Press, Cambridge.

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