Numerical Simulations of Reaching a Steady State: No Need to Generate Any Rational Expectations
It is not easy to numerically simulate the path to a steady state because there is no closed form solution in dynamic economic growth models in which households behave generating rational expectations. In contrast, it is easy if households are supposed to behave under the MDC (maximum degree of comfortability)-based procedure. In such a simulation, a household increases or decreases its consumption according to simple formulae. In this paper, I simulate the path when households behave under the MDC-based procedure, and the results of simulations indicate that households can easily reach a stabilized (steady) state without generating any rational expectations by behaving according to their feelings and guesses about their preferences and the state of the entire economy.
Harashima, T. 2022. Numerical simulations of reaching a steady state: No need to generate any rational expectations. Journal of Applied Economic Sciences, Volume XVII, Winter, 4(78): 321 – 339. https://doi.org/10.57017/jaes.v17.4(78).04
[1] Becker, R.A. (1980). On the Long-run Steady State in a Simple Dynamic Model of Equilibrium with Heterogeneous Households, The Quarterly Journal of Economics, 95(2): 375–382. https://doi.org/10.2307/1885506
[2] Blanchard, O.J. and Kahn, Ch.M. (1980). The Solution of Linear Difference Models under Rational Expectations, Econometrica, 48(5): 1305-1311. https://doi.org/10.2307/1912186
[3] Ellison, M., and Pearlman, J. (2011). Saddlepath Learning, Journal of Economic Theory, 146(4): 1500-1519. https://doi.org/10.1016/j.jet.2011.03.005
[4] Evans, G.W., and Seppo, H. (2001). Learning and Expectations in Macroeconomics, Princeton and Oxford, Princeton University Press, 424 pp. ISBN: 978-0691049212
[5] Harashima, T. (2010). Sustainable Heterogeneity: Inequality, Growth, and Social Welfare in a Heterogeneous Population, MPRA Paper No. 24233. https://mpra.ub.unimuenchen.de/22521/1/MPRA_ paper_22521.pdf
[6] Harashima, T. (2012). Sustainable Heterogeneity as the Unique Socially Optimal Allocation for Almost All Social Welfare Functions, MPRA Paper No. 40938. https://mpra.ub.unimuenchen.de/40938/1/MPRA_paper _40938.pdf
[7] Harashima, T. (2014). Sustainable Heterogeneity in Exogenous Growth Models: The Socially Optimal Distribution by Government’s Intervention, Theoretical and Practical Research in Economic Fields,5(1):73-100.
[8] Harashima, T. (2017). Sustainable Heterogeneity: Inequality, Growth, and Social Welfare in a Heterogeneous Population, in Japanese, Journal of Kanazawa Seiryo University, 51(1): 31-80.
[9] Harashima, T. (2018). Do Households Actually Generate Rational Expectations? “Invisible Hand” for Steady State, MPRA Paper No. 88822. https://core.ac.uk/download/pdf/ 214008545.pdf
[10] Harashima, T. (2019). Do Households Actually Generate Rational Expectations? “Invisible Hand” for Steady State, in Japanese, Journal of Kanazawa Seiryo University, 52(2): 49-70.
[11] Harashima, T. (2020). Sustainable Heterogeneity as the Unique Socially Optimal Allocation for Almost All Social Welfare Functions, in Japanese, Journal of Kanazawa Seiryo University, 54(1): 71-95. https://mpra. ub.uni-muenchen.de/40938/1/MPRA_paper_40938.pdf
[12] Harashima, T. (2021) Consequence of Heterogeneous Economic Rents under the MDC-based Procedure, Journal of Applied Economic Sciences, Volume XVI, Summer, 2(72): 185-190. https://mpra.ub.uni-muenchen.de/105765/1/ MPRA_paper_105765.pdf
[13] Harashima, T. (2022a). A Theory of Inflation: The Law of Motion for Inflation under the MDC-based Procedure, MPRA Paper No. 113161. https://mpra.ub.uni-muenchen.de/113161/4/ MPRA_paper_113161.pdf
[14] Harashima, T. (2022b). A Theory of Inflation: The Law of Motion for Inflation under the MDC-based Procedure, in Japanese, Journal of Kanazawa Seiryo University, 54(1).
[15] Kydland, F.E., and Prescott, E.C. (1982). Time to Build and Aggregate Fluctuations, Econometrica, 50(6): 1345-1370. https://doi.org/10.2307/1913386
[16] Marcet, A.l., and Sargent, T.J. (1989). Convergence of Least Squares Learning Mechanisms in Self-referential Linear Stochastic Models, Journal of Economic Theory, 48(2): 337-368. https://doi.org/10.1016/ 00220531(89)90032-X
[17] Uhlig, H. (2001). A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily, in Computational Methods for the Study of Dynamic Economies, Ch. 3, Marimon, R. (Ed.), Scott, A. (Ed.), Oxford University Press. https://www.sfu.ca/~kkasa/uhlig1.pdf