政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/60322
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 109948/140897 (78%)
造访人次 : 46107153      在线人数 : 1336
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/60322


    题名: 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
    贡献者: 陳樹衡
    Chen, Shu Heng
    張嘉玲
    Chang, Chia Ling
    关键词: 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
    上传时间: 2013-09-05 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.
    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.

    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.


    Kirman, A. (1991) Epidemics of opinion and speculative bubbles in financial markets. In: Taylor M (ed.), Money and Financial Markets, Blackwell, Cambridge, pp. 354-368.

    Kirman, A. (1993), Ants, rationality and recruitment, The Quarterly Journal of Economics, 108:137-156.

    Krugman, P. (2009), How did economists get it so wrong?,The New York Times, September 6.

    Kullback, S. and R. A. Leibler (1951), On information and sufficiency, Annals of Mathematical Statistics, 22:76-86.

    Lengnick, M. and H. C. Wohltmann (2010), Agent-based financial markets and New Keynesian macroeconomics: a synthesis, Working Paper.

    Lewbel, A. (1989), Identification and estimation of equivalence scales under weak separability, Review of Economic Studies, 56:311-16.

    Lewbel, A. (1994), Aggregation and simple dynamics, American Economic Review, 84:905-918.

    Liljeros, F., C. R. Edling, L. A. N. Amaral, H. E. Stanley, and Y. Åberg(2001), The web of human sexual contacts, Nature, 411:907–908.

    Lippi, M. (1988), On the dynamic shape of aggregated error correction models, Journal of Economic Dynamics and Control, 12:561-585.

    Luce, R. (1959), Individual Choice Behavior: A Theoretical Analysis, Wiley Press, NY.

    Milani, F. (2009), Adaptive learning and macroeconomic inertia in the Euro area, Journal of Common Market Studies, 47: 579 – 599

    Nelson, E. (2001), What does the UK’s monetary policy and inflation experience tell us
    about the transmission mechanism?, CEPR Working Paper No. 3047.

    Nelson, E. (2002), Direct effects of base money on aggregate demand: theory and evidence, Journal of Monetary Economics, 49: 687-708.

    Orphanides, A., and J. C. Williams (2007a), Imperfect knowledge, inflation expectations and monetary policy in: Bernanke, M., Woodford, M.(ed.), The Inflation Targeting Debate, University of Chicago Press, pp. 201-246.

    Orphanides, A., and J. C. Williams (2007b), Robust monetary policy with imperfect knowledge, Journal of Monetary Economics, 54:1406-1435.

    Peersman G. und F. Smets (1999), The Taylor Rule: A Useful Monetary Policy Benchmark for the Euro Area?, International Finance, 1:85-116.

    Redner, S. (1998), How popular is your paper? An empricial study of the citation distribution, The European Physical Journal, B4:131–134.

    Rudebusch, G. and L. Svensson, (1999), Policy Rules for Inflation Targeting, in: J. Taylor(ed.), Monetary Policy Rules, University of Chicago Press for NBER.

    Schiavo, S., J. Reyes, and G. Fagiolo (2010), International trade and financial integration: a weighted network analysis, Quantitative Finance, 10:389–399

    Shannon, C. E. (1948), A mathematical theory of communication, The Bell System Technical Journal, 27:379-423.

    Spaventa, L.(2009), Economists and Economics: what does the crisis tell us?, Centre for Economic Policy Research Policy Insight,No.38.

    Stoker, T.M. (1984), Completeness, distribution restriction, and the form of aggregate functions, Econometrica, 52:887–907.

    Theil, H. (1954), Linear aggregation of economic relations, North Holland Press, Amsterdam.

    Trivedi, P. K. (1985), Distributed lags, aggregation and compounding: some econmetric implications, Review of Economic Studies, 52:19-35.

    Vega-Redondo, F. (2007), Complex Social Networks, Econometric Society Monograph Series, Cambridge University Press, Cambridge.

    Wen, Y. (2010), Liquidity and welfare in a heterogeneous agent economy, Working Paper.

    Watts, D. J. and S. H. Strogatz (1998), Collective dynamics of ‘small-world’ networks. Nature, 393:440–442.

    Westerhoff, F. (2010), An agent-based macroeconomic model with interacting firms, socio-economic opinion formation and optimistic/pessimistic sales expectations, Working Paper.
    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
    数据类型: thesis
    显示于类别:[經濟學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    850801.pdf283KbAdobe PDF2386检视/开启
    850802.pdf206KbAdobe PDF2286检视/开启
    850803.pdf2269KbAdobe PDF2422检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈