Monetary policy aimed at controlling economies focuses on manipulating interest rates in an effort to manage the output, inflation price level, and activity. Our study analyzes the effects of monetary policy from 1980 to 2023 using Bayesian estimation methods across four macroeconomic models: a calibrated New-Keynesian model, Federal Reserve’s FRB model, a New Keynesian DSGE model and Smets and Wouters model. Simulation concentrates on three policy rules such as Taylor, LWW and SW, and how they affect significant variables such as real GDP, inflation and investment. Differences in the response patterns to monetary policy changes are captured by all models; however, the degree of output elasticity and the time taken by it to respond differently to a cut in interest rates is observed to be different. New-Keynesian model demonstrates a sudden and a temporary rise in output, the FRB and DSGE models reveal prolonged but slow changes and the adjustment takes longer, according to the FRB model. Inflation responses that are pro-cyclical are also persistent across models and affected by the price stickiness effect. Models with financial accelerators such as the DSGE provide better insights of the relations between interest rates and borrowing costs. Lastly, sensitivity analysis also reveals that model structure and policy rule decision significantly affects the results, consistent with the notion that monetary policy should be well suited to economic environment.
Keywords
Monetary Policy, Bayesian Estimation, Macroeconomic Models and Policy Rules, Dynamic Stochastic General Equilibrium Models, Impulse Response Functions.
B. T. McCallum, “A monetary policy rule for automatic prevention of a liquidity trap,” in University of Chicago Press eBooks, 2006, pp. 9–38. doi: 10.7208/chicago/9780226379012.003.0002.
R. Ahrend, P. A. Catte, and R. Price, “Interactions between Monetary and Fiscal Policy: How Monetary Conditions Affect Fiscal Consolidation,” SSRN Electronic Journal, Jan. 2006, doi: 10.2139/ssrn.1010655.
N. Medvidovic and R. N. Taylor, “A classification and comparison framework for software architecture description languages,” IEEE Transactions on Software Engineering, vol. 26, no. 1, pp. 70–93, Jan. 2000, doi: 10.1109/32.825767.
A. Orphanides, “Monetary policy rules based on Real-Time data,” American Economic Review, vol. 91, no. 4, pp. 964–985, Sep. 2001, doi: 10.1257/aer.91.4.964.
K. J. Lansing, “Time-varying U.S. inflation dynamics and the New Keynesian Phillips curve,” Review of Economic Dynamics, vol. 12, no. 2, pp. 304–326, Jul. 2008, doi: 10.1016/j.red.2008.07.002.
C. L. Schultze, Recent inflation in the United States. 1959. [Online]. Available: http://ci.nii.ac.jp/ncid/BA3751126X
“Historical approaches to monetary policy.” https://www.federalreserve.gov/monetarypolicy/historical-approaches-to-monetary-policy.htm
M. Beiki, A. Bashari, and A. Majdi, “Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network,” International Journal of Rock Mechanics and Mining Sciences, vol. 47, no. 7, pp. 1091–1103, Aug. 2010, doi: 10.1016/j.ijrmms.2010.07.007.
C. A. Sims, “The role of models and probabilities in the monetary policy process,” Brookings Papers on Economic Activity, vol. 2002, no. 2, pp. 1–62, Jan. 2002, doi: 10.1353/eca.2003.0009.
T. H. Smitha and K. C. DrSankaranarayanan, “Impact of monetary policy on Indian economy in the Post-Reform Period,” 2010. [Online]. Available: https://dyuthi.cusat.ac.in/xmlui/bitstream/purl/2159/3/Dyuthi-T0517.pdf
O. Canuto and M. Cavallari, “Asset prices, macro prudential regulation, and monetary policy,” World Bank Publications, no. 116, pp. 1–8, May 2013, [Online]. Available: https://openknowledge.worldbank.org/bitstream/10986/16116/1/774530BRI0EP1160Box377297B00PUBLIC0.pdf
E. C. Prescott, “Theory ahead of business-cycle measurement,” Carnegie-Rochester Conference Series on Public Policy, vol. 25, pp. 11–44, Sep. 1986, doi: 10.1016/0167-2231(86)90035-7.
B. Friedman and K. Kuttner, “Implementation of monetary policy: How do central banks set interest rates?,” Jul. 2010. doi: 10.3386/w16165.
A. Dogan, “Investment specific technology shocks and emerging market business cycle dynamics,” Review of Economic Dynamics, vol. 34, pp. 202–220, Apr. 2019, doi: 10.1016/j.red.2019.03.012.
J. Crotty, “Innocence ou dissidence desAmants magnifiquesde Molière?,” Seventeenth-Century French Studies, vol. 31, no. 2, pp. 151–161, May 2008, doi: 10.1179/102452908x289811.
B. J. Heijdra and C. G. F. Van Der Kwaak, “Some unconventional properties of new Keynesian DSGE models,” De Economist, vol. 171, no. 2, pp. 139–183, May 2023, doi: 10.1007/s10645-023-09420-4.
R. Marimon, S. E. Spear, and S. Sunder, “Expectationally driven market volatility: an experimental study,” Journal of Economic Theory, vol. 61, no. 1, pp. 74–103, Oct. 1993, doi: 10.1006/jeth.1993.1059.
O. Goudet, J.-D. Kant, and G. Ballot, “WorkSIM: a calibrated Agent-Based model of the labor market accounting for workers’ stocks and gross flows,” Computational Economics, vol. 50, no. 1, pp. 21–68, Jul. 2016, doi: 10.1007/s10614-016-9577-0.
A. G. C. Wright, R. F. Krueger, M. J. Hobbs, K. E. Markon, N. R. Eaton, and T. Slade, “The structure of psychopathology: Toward an expanded quantitative empirical model.,” Journal of Abnormal Psychology, vol. 122, no. 1, pp. 281–294, Oct. 2012, doi: 10.1037/a0030133.
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Valeriya Chekalina
Valeriya Chekalina
Kutafin Moscow State Law University, Moscow, Russia, 123001.
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Cite this article
Valeriya Chekalina, “Comparative Analysis of Monetary Policy using Bayesian Estimation: Macroeconomic Models and Policy Rules”, Journal of Enterprise and Business Intelligence, vol.4, no.1, pp. 051-060, January 2024. doi: 10.53759/5181/JEBI202404006.
