Abstract
A new Keynesian model built on an agent-based approach is considered and employed to investigate China’s monetary policy and macroeconomic fluctuations. The assumption of perfect rationality used in standard dynamic stochastic general equilibrium (DSGE) models is abandoned. The expectation’s heterogeneity, caused by agents behaving according to individual rules through adaptive learning, is one of the agent-based model (ABM) characteristics inserted into the DSGE model. Differential evolution (DE) algorithm is employed to estimate the parameters of an agent-based new Keynesian (ABNK) model, which combined the ABM and the new Keynesian DSGE models. The primary contribution of this study is that the degree of rationality in the economy has been estimated using a model with heterogeneous bounded rationality and adaptive learning. In addition, the determinacy properties of ABNK models with different degrees of heterogeneity are analyzed, which shows that the models that are determinate under the assumptions of rationality may become indeterminate in the presence of heterogeneous expectations.
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References
Adam, K. (2007). Experimental evidence on the persistence of output and inflation. The Economic Journal, 117(520), 603–636.
Agliari, A., Delli Gatti, D., Gallegati, M., & Lenci, S. (2006). The complex dynamics of financially constrained heterogeneous firms. Journal of Economic Behavior and Organization, 61(4), 784–803.
Anufriev, M., Assenza T., Hommes C., & Massaro D. (2008). Interest rate rules and macroeconomic stability under heterogeneous expectations. CeNDEF Working Paper, University of Amsterdam.
Assenza, T., Heemeijer, P., Hommes, C., Massaro D. (2011). Individual expectations and aggregate macro behavior. CeNDEF Working Paper, University of Amsterdam.
Berardi, M. (2007). Heterogeneity and misspecifications in learning. Journal of Money, Credit and Banking, 31, 3203–3227.
Branch, W. (2004). The theory of rationally heterogeneous expectations: Evidence from survey data on inflation expectations. The Economic Journal, 114(497), 592–621.
Branch, W., & McGough, B. (2009). A new Keynesian model with heterogeneous expectations. Journal of Economic Dynamics and Control, 33, 1036–1051.
Brazier, A., Harrison, R., King, M., & Yates, T. (2008). The danger of inflating expectations of macroeconomic stability: Heuristic switching in an overlapping generations monetary model. International Journal of Central Banking, 4(2), 219–254.
Brock, W., & de Fontnouvelle, P. (2000). Expectational diversity in monetary economies. Journal of Economic Dynamic sand Control, 24(5–7), 725–759.
Calvo, G. A. (1983). Staggered contracts in a utility maximizing framework. Journal of Monetary Economics, 12, 383–398.
Carroll, C. (2003). Macroeconomic expectations of households and professional forecasters. Quarterly Journal of Economics, 118, 269–298.
Canova, F. (2005). Methods for applied macroeconomic research. Princeton: Princeton University Press.
Cincotti, S., Raberto, M., & Teglio, A. (2010). Credit money and macroeconomic instability in the agent-based model and simulator eurace. Economics, 3, 2010–2026.
Dawid, H., & Neugart, M. (2011). Agent-based models for economic policy design. Eastern Economic Journal, 37(1), 44–50.
Dawid, H., Gemkow, S., Harting, P., & Neugart, M. (2009). On the effects of skill upgrading in the presence of spatial labor market frictions: An agent-based analysis of spatial policy design. Journal of Artificial Societies and Social Simulation, 12(4), 5.
De Grauwe, P. (2010). Animal spirit sand monetary policy, economic theory.
Deissenberg, C., van der Hoog, S., & Dawid, H. (2008). Eurace: A massively parallel agent-based model of the european economy. Applied Mathematics and Computation, 204, 541–552.
Delli Gatti, D., Di Guilmi, C., Gaffeo, E., Giulioni, G., Gallegati, M., & Palestrini, A. (2005). A new approach to business fluctuations: Heterogeneous interacting agents, scaling laws and financial fragility. Journal of Economic Behavior and Organization, 56(4), 489–512.
Delli Gatti, D., Gaffeo, E., Gallegati, M., Giulioni, G., Kirman, A., Palestrini, A., et al. (2007). Complex dynamics and empirical evidence. Information Sciences, 177(5), 1204–1221.
Delli Gatti, D., Gaffeo, E., & Gallegati, M. (2010). Complex agent-based macroeconomics: a manifesto for a new paradigm. Journal of Economic Interaction and Coordination, 5(2), 111–135.
Den Haan, W. (2010). Comparison of solutions to the incomplete markets model with aggregate uncertainty. Journal of Economic Dynamics and Control, 34(1), 4–27.
Dhyne, Emmanuel, et al. (2005). Price setting in the euro area: Some stylized facts from individual consumer price data. European Central Bank, Working paper No. 524.
Dilaver, O., Jump, R., & Levine, P. (2016). Agent-based macroeconomics and dynamic stochastic general equilibrium models: Where Do we go from here. School of Economics Discussion Papers, 1–23.
Dixit, A. K., & Stiglitz, J. E. (1997). Monopolistic competition and optimum product diversity. The American Economic Review, 67(3), 297–308.
Dosi, G., Fagiolo, G., & Roventini, A. (2006). An evolutionary model of endogenous business cycles. Computational Economics, 27(1), 3–34.
Dosi, G., Fagiolo, G., & Roventini, A. (2008). The microfoundations of business cycles: An evolutionary, multi-agent model. Journal of Evolutionary Economics, 18, 413–432.
Dosi, G., Fagiolo, G., & Roventini, A. (2010). Schumpeter meeting keynes: A policy-friendly model of endogenous growth and business cycle. Journal of Economic Dynamics and Control, 34(9), 1748–1767.
Dosi, G., Fagiolo, G., Napoletano, M., & Roventini, A. (2013). Income distribution, credit and fiscal policies in an agent-based Keynesian model. Journal of Economic Dynamics and Control, 37(8), 1598–1625.
Elsner, W., Heinrich, T., & Schwardt, H. (2015). The microeconomics of complex economies. Amsterdam: Elsevier.
Evans, G. W., & Honkapohja, S. (2001). Learning and expectations in macroeconomics. Princeton: Princeton University Press.
Evans, G., & Honkapohja, S. (2003). Expectations and the stability problem for optimal monetary policies. Review of Economic Studies, 70, 807–824.
Evans, G., & Honkapohja, S. (2006). Monetary policy, expectations and commitment. The Scandinavian Journal of Economics, 108, 15–38.
Gaffeo, E., Delli Gatti, D., Desiderio, S., & Gallegati, M. (2008). Adaptive microfoundations for emergent macroeconomics. Eastern Economic Journal, 34, 441–463.
Gali, J. (2007). Monetary policy, inflation, and the business cycle. Unpublished monograph.
Gali, J., & Gertler, M. (1999). Inflation dynamics: A structural econometric analysis. Journal of Monetary Economics, 44, 195–222.
Gilli, M., & Schumann, E. (2009). Heuristic optimisation in financial modelling. COMISEF wps-007 09/02/2009.
Gilli, M., & Winker, P. (2003). A global optimization heuristic for estimating agent based models. Computational Statistics and Data Analysis, 42, 299–312.
Gobbi, G., (2016). A basic new Keynesian DSGE model with heterogeneous learning: An agent-based approach. Working paper.
Gourieroux, C., & Monfort, A. (1996). Simulation-based econometric methods. Oxford: Oxford University Press.
Gourieroux, C., Monfort, A., & Renault, E. (1993). Indirect inference. Journal of Applied Econometrics, 8, 85–118.
Grabner, C. (2015). How agent-based modeling and simulation relates to CGE and DSGE modeling. In 2014 IEEE conference on computational intelligence for financial engineering and economics (CIFEr), London, pp. 349–356.
Gregory, A., & Smith, G. (1991). Calibration as testing: Inference in simulated macro models. Journal of Business and Economic Statistics, 9, 293–303.
Hommes, C., Sonnemans, J., Tuinstra, J., & van de Velden, H. (2005). Coordination of expectations in asset pricing experiments. Review of Financial Studies, 18(3), 955–980.
Hommes, C. H. (2011). The heterogeneous expectations hypothesis: Some evidence from the lab. Journal of Economic Dynamics and Control, 35, 1–24.
Holland, J. H. (1975). Adaptation in natural artificial systems. Ann Arbor: University of Michigan Press.
Krink, P. S. (2011). Multiobjective optimization using differential evolution for real-world portfolio optimization. Computational Management Science, 8, 157–179.
Le, V. P. M., Meenagh, D., Minford, P., & Wickens, M. (2011). How much nominal rigidity is there in the US economy–testing a new Keynesian model using indirect inference. Journal of Economic Dynamics and Control, 35(12), 2078–2104.
Le, V. P. M., & Meenagh, D. (2013). Testing and estimating models using indirect inference. Cardiff Economics Working Paper No. E2013/8.
Mankiw, N., Reis, R., & Wolfer, J. (2003). Disagreement about inflation expectations. NBER Macroeconomics Annual, 18, 209–248.
Maringer, D. G., & Meyer, M. (2008). Smooth transition autoregressive models: New approaches to the model selection problem. Studies in Nonlinear Dynamics and Econometrics, 12(1), 1–19.
Massaro, D. (2013). Heterogeneous expectations in monetary DSGE model. Journal of Economic Dynamics and Control, 37(3), 680–692.
Nimark, K. (2009). A structure model of australia as a small open economy. The Australian Economic Review, 42(01), 24–41.
Pfajfar, D., & Santoro, E. (2003). Heterogeneity, learning and information stickiness in inflation expectations. Journal of Economic Behavior and Organization, 75(3), 426–444.
Pfajfar, D. (2008). Heterogeneous expectations in macroeconomics. Phd thesis, University of Cambridge.
Price, K. V., Storn, R. M., & Lampinen, J. A. (2005). Differential evolution: A practical approach to global optimization. Berlin: Springer.
Price, K. V., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: A practical approach to global optimization (2d ed.). Berlin: Springer.
Raberto, M., Teglio, A., & Cincotti, S. (2011). Debt deleveraging and business cycles: An agent- based perspective. Economics Discussion Paper, 2011-31.
Ricetti, L., Russo, A., & Gallegati, M. (2013). Unemployment benefits and financial leverage in an agent based macroeconomic model. Economics 2013-42.
Russo, A., Catalano, M., Gaffeo, E., Gallegati, M., & Napoletano, M. (2007). Industrial dynamics, fiscal policy and r&d: Evidence from a computational experiment. Journal of Economic Behavior and Organization, 64, 426–447.
Sargent, T. J. (1999). The conquest of American inflation. Princeton: Princeton University Press.
Smith, Jr., A. A. (1990). Three essays on the solution and estimation of dynamic macroeconomic models. Ph.D. dissertation, Duke University.
Smith, A. A, Jr. (1993). Estimating nonlinear time series models using simulated vector autoregressions. Journal of Applied Econometrics, 8, 63–84.
Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
Tuinstra, J., & Wagener, F. (2007). On learning equilibria. Economic Theory, 30(3), 493–513.
Winberry, T. (2016). A toolbox for solving and estimating heterogeneous agent macro models. Working paper.
Acknowledgements
We thank Hans Amman (the editor in charge), anonymous referees, Zhiwei Xu and Jun Wen for their helpful comments and suggestions. Wei Zhao acknowledges the financial support from the Doctoral Research Foundation of Northwest A&F University (No. Z109021803). Genfu Feng acknowledges the financial support from the National Social Science Foundation of China (No. 14BJY00).
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Appendices
Appendix
A.1 Derivation of Consumption Rule
The Lagrangian function is given by:
Then, we have the first-order conditions of household \(i_h\):
Eliminating the multipliers, we obtain:
which can be log-linearized around the steady state to obtain:
The Euler equation of household \(i_h\) is as follows:
where \(b_t = \exp \left\{ { - i_t } \right\} \). Note that \(i_t\) corresponds to the logarithm of the gross yield on the one-period bond; henceforth, this is referred to as the nominal interest rate. (The yield on the one-period bond is defined by \(b_t = \left( {1 + yield_t } \right) ^{ - 1}\). Note that \(i_t = - \log b_t = \log \left( {1 + yield_t } \right) ^{ - 1} \simeq yield_t\), where the latter approximation will be accurate as long as the nominal yield is “small”.) Substituting this into the above equation, we obtain:
In the zero-inflation steady state, we have:
The above equation can be rewritten as:
Thus, the consumption Euler equation can be log-linearized as follows:
The money demand of household \(i_h\) is:
Defining the constant money stock as and substituting \(b_t = \exp \left\{ { - i_t } \right\} \) into it, we obtain:
In the steady state, we obtain:
Taking the logarithm of the above equation, we have:
By defining , we obtain the linearized money demand function as follows:
where \(m_t^g = m_t - g_m t\).
A.2 Derivation of Final-Goods Firms Problem
The maximization problem of final-goods firms is:
The first-order condition for the problem is given by:
Rearranging the terms, we finally obtain:
The above equation is the demand curve of the intermediate-goods firm \(i_f\). Plugging it into the profit function of the final-goods firm, we obtain:
Simplifying the above equation, we obtain:
A.3 Derivation of Eq. (14)
Firms that re-optimize their prices will choose the same price \(P_t^*\). By Eq. (13), we obtain:
Dividing both sides of the above equation by \(P_{t - 1}\) gives us:
Considering a zero-inflation steady state (\({\overline{\prod }} = 1\) and ) around which we linearize the above equation, we obtain:
Thus, we finally obtain:
A.4 Derivation of Eq. (16)
Note that:
Thus:
A.5 Derivation of Eq. (18)
In the zero-inflation steady state, we obtain , \(\prod _{t - 1,t + k} = 1\), \(P_t^* = P_{t + k}\), \(Y_{t + k|t} = Y\) and \(MC_{t + k|t} = MC\). We also obtain \(\Delta _{t,t + k} = \beta ^k\). Thus, in the steady state, we have:
From above equation, it is obtained that . Eq. (17) can be log-linearized to obtain:
We know \(E_t {\hat{p}}_t^* = {\hat{p}}_t^*\), \(E_t {\hat{p}}_{t - 1} = {\hat{p}}_{t - 1}\) and , so:
The above equation can be rewritten as:
A.6 Derivation of Eq. (29)
First, we obtain:
where \({\overline{M}} _t\) denotes the money stock in the economy. Dividing both sides of the above equation by the total effective labor, we have:
Taking the logarithm of both sides and noting that \(\ln \left( {1 + \gamma } \right) \approx \gamma \), we have:
where \(\Delta m_t = \Delta {\bar{m}}_t - \gamma \). Substituting \(m_t^g = m_t - g_m t\) into the above equation, we obtain:
where \(\Delta m_t^g = \Delta m_t - g_m = \Delta {\bar{m}}_t - \gamma - g_m\). By substituting \(h_t^g \equiv m_t^g - p_t\) into the above equation, we obtain:
A.7 Index of the Parameters
First, we obtain:
where \({\overline{M}} _t\) denotes the money stock in the economy. Dividing both sides of the above equation by the total effective labor, we have:
Taking the logarithm of both sides and noting that \(\ln \left( {1 + \gamma } \right) \approx \gamma \), we have:
where \(\Delta m_t = \Delta {\bar{m}}_t - \gamma \). Substituting \(m_t^g = m_t - g_m t\) into the above equation, we obtain:
where \(\Delta m_t^g = \Delta m_t - g_m = \Delta {\bar{m}}_t - \gamma - g_m\). By substituting \(h_t^g \equiv m_t^g - p_t\) into the above equation, we obtain:
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Zhao, W., Lu, Y. & Feng, G. How Many Agents are Rational in China’s Economy? Evidence from a Heterogeneous Agent-Based New Keynesian Model. Comput Econ 54, 575–611 (2019). https://doi.org/10.1007/s10614-018-9844-3
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DOI: https://doi.org/10.1007/s10614-018-9844-3