学部後期課程
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最終更新日:2024年4月22日

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マクロ経済動学

Macroeconomic Dynamics: Heterogeneity and Interaction
The standard macroeconomic theory is based on dynamic general equilibrium, and its framework has been vigorously extended to the environments where heterogeneous agents interact with each other. This course provides a self-contained introduction to this literature. As an application, we focus on power-law distributions, which emerge in heterogeneities of microeconomic agents and also in fluctuations of macroeconomic variables. For example, household income and wealth distributions, firm size distribution, and the distribution of asset price fluctuations all follow power-law distributions. This course demonstrates that the dynamic general equilibrium models can incorporate power-law distributions of both heterogeneous agents and aggregate fluctuations.
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時間割/共通科目コード
コース名
教員
学期
時限
0704177
FEC-EC5801L3
マクロ経済動学
楡井 誠
S1 S2
木曜1限
マイリストに追加
マイリストから削除
講義使用言語
英語
単位
2
実務経験のある教員による授業科目
NO
他学部履修
開講所属
経済学部
授業計画
I. Dynamic General Equilibrium 1. Introduction. I will describe the aim and scope of this course and explain the prerequisite materials. I briefly review dynamic programming, dynamical systems, Markov processes, and stationary distribution. 2. Dynamic general equilibrium model. The model is explained using the stochastic optimal growth model and competitive equilibrium. Welfare theorems, risk sharing, and the role of finance are explained, following Ljungqvist and Sargent (2018). 3. Heterogeneous-agent model. A basic model of heterogeneous agents in dynamic general equilibrium is introduced using a firm heterogeneity model by Hopenhayn (1992). Its applications are explained by using a trade model by Melitz (2003). 4. Income and wealth disparity. The incomplete-markets model of Aiyagari (1994) is explained, and its implications on consumption and saving are detailed using Krusell and Smith (1998). II. Power Laws 5. Power laws of household income and wealth. Tail distributions of household income and wealth follow Pareto distributions, i.e., power-law distributions. Using a modified Aiyagari model (Nirei and Aoki 2016), the intuition and significance of the power-law tails are explained. 6. Power laws of firm size. The skewed distribution of firm size is explained by using the innovation models of Klette and Kortum (2004), Lentz and Mortensen (2008), Luttmer (2007, 2011), and Aoki and Nirei (2017). 7. Power law distributions. I review several mechanisms that generate power laws. Examples include random growth models, generalized central limit theorems, extreme value theorems, and critical phenomena. 8. Granular firms. As an application of the generalized central limit theorem, the granular hypothesis of aggregate fluctuations by Gabaix (2011) is introduced. Relations to Hulten’s theorem are explained, and implications beyond Hulten’s theorem are drawn by referring to Baqaee and Farhi (2019) and Carvalho et al. (2021). III. Synchronization. 9. Interactions and aggregate fluctuations. Firm-level interactions can be a source of aggregate-level fluctuations. This point is explained using Acemoglu et al. (2012), who highlight the heterogeneity of the input-output network. 10. Introduction to continuous-time process and optimization. Heterogeneous-agent, continuous-time modeling is reviewed using Achdou et al. (2021). 11. Power-law synchronization in (S,s) models. A tractable (S,s) model of menu-cost pricing is presented. I explain analytics of stochastic synchronization generated in this environment and that the synchronization exhibits power-law fluctuations, drawing on Nirei and Scheinkman (2024 forthcoming). 12. Self-organized criticality. The power-law synchronizations emerge in a class of models known as self-organized criticality. Models of firms’ investment and of investor herding are presented to show how the self-organized criticality may occur in economic equilibria.
授業の方法
Lectures
成績評価方法
Term paper and/or presentation
教科書
Readings are assigned each week.
参考書
1. Acemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz‐Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80(5), 1977-2016. 2. Achdou, Y., Han, J., Lasry, J. M., Lions, P. L., & Moll, B. (2022). Income and wealth distribution in macroeconomics: A continuous-time approach. The Review of Economic Studies, 89(1), 45-86. 3. Aiyagari, S. R. (1994). Uninsured idiosyncratic risk and aggregate saving. The Quarterly Journal of Economics, 109(3), 659-684. 4. Aoki, S., & Nirei, M. (2017). Zipf's law, Pareto's law, and the evolution of top incomes in the United States. American Economic Journal: Macroeconomics, 9(3), 36-71. 5. Gabaix, X. (2011). The granular origins of aggregate fluctuations. Econometrica, 79(3), 733-772. 6. Hopenhayn, H. A. (1992). Entry, exit, and firm dynamics in long run equilibrium. Econometrica, 1127-1150. 7. Klette, T. J., & Kortum, S. (2004). Innovating firms and aggregate innovation. Journal of Political Economy, 112(5), 986-1018. 8. Krusell, P., & Smith, Jr, A. A. (1998). Income and wealth heterogeneity in the macroeconomy. Journal of Political Economy, 106(5), 867-896. 9. Lentz, R., & Mortensen, D. T. (2008). An empirical model of growth through product innovation. Econometrica, 76(6), 1317-1373. 10. Ljungqvist, L. & Sargent, T.J. (2018). Recursive Macroeconomic Theory, fourth edition. MIT. 11. Luttmer, E. G. (2007). Selection, growth, and the size distribution of firms. The Quarterly Journal of Economics, 122(3), 1103-1144. 12. Luttmer, E. G. (2011). On the mechanics of firm growth. The Review of Economic Studies, 78(3), 1042-1068. 13. Melitz, M. J. (2003). The impact of trade on intra‐industry reallocations and aggregate industry productivity. econometrica, 71(6), 1695-1725. 14. Nirei, M., & Aoki, S. (2016). Pareto distribution of income in neoclassical growth models. Review of Economic Dynamics, 20, 25-42. 15. Nirei, M., & Scheinkman, J. A. (2024). Repricing avalanches. Journal of Political Economy, forthcoming. 16. Sornette, D. (2006). Critical Phenomena in Natural Sciences, second edition. Springer. 17. Baqaee, D. R., & Farhi, E. (2019). The macroeconomic impact of microeconomic shocks: Beyond Hulten's theorem. Econometrica, 87(4), 1155-1203. 18. Carvalho, V. M., Nirei, M., Saito, Y. U., & Tahbaz-Salehi, A. (2021). Supply chain disruptions: Evidence from the Great East Japan Earthquake. The Quarterly Journal of Economics, 136(2), 1255-1321.
履修上の注意
It is a prerequisite for this course that students have solid understandings of introductory macroeconomics, microeconomics, and calculus.