The Implementation of Monetary Policy in an Emerging Economy: The Case of Chile
Keywords:
monetary policy, neural networks, policy indicators, central bank, ChileAbstract
Central bank authorities base implementation of monetary policy on an analysis of multiple variables known as monetary policy indicators. In a small open economy such as Chile, these indicators may include in-flation misalignments, unemployment, GDP growth, money growth, the current account balance, exchange rate volatility and international re-serves. A neural network approach is used to establish the correspond-ing weights considered by the Board of the Central Bank of Chile dur-ing the period 1995-2003. GDP growth and the difference between the actual and the target inflation were found to be among the variables of greatest weight in the monetary policy decision-making process of the Central Bank of Chile during this period.Downloads
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Baker, B. D., & Richards, C. E. (1999). A comparison of conventional linear regression methods and neural networks for forecasting educational spending. Economics of Education Review, 18(4), 405–415. https://doi.org/10.1016/s0272-7757(99)00003-5
Bianchi, A. (1994). La autonomía del Banco central de Chile. Testimonios sobre la Actuación de la Banca Central, Centro de Estudios Monetarios Latinoamericanos, México D.F.
Cabrera, A., & Lagos, L. F. (2002). Monetary policy in Chile: A black box. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges, Central Bank of Chile, Santiago, 197–245.
Calderón, C., & Duncan, R. (2003). The role of credibility in the cyclical properties of macroeconomic policies in emerging economies. Working Paper Nº 237, Central Bank of Chile, November.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press. https://doi.org/10.1515/9781400830213
Church, K. B., & Curram, S. P. (1996). Forecasting consumers' expenditure: A comparison between econometric and neural network models. International Journal of Forecasting, 12(2), 255–267. https://doi.org/10.1016/0169-2070(95)00631-1
Corbo, V. (2002). Monetary policy in Latin America in the 1990s. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges, Central Bank of Chile, Santiago, 117–165.
Corbo, V., Landerretche, O., & Schmidt-Hebbel, K. (2002). Does inflation targeting make a difference. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges, Central Bank of Chile, Santiago, 221–269.
De Gregorio, J. (2003). Dinero e inflación: ¿En qué estamos? Economía Chilena, 6(1), 3–19.
Edwards, S., & Vergara, R. (2002). Fiscal sustainability, debt dynamics and debt relief: The cases of nicaragua and honduras. Unpublished manuscript, October.
Franses, P., & Van Dijk, D. (2000). Non-linear time Series models in empirical finance. Cambridge University Press.
Freeman, J. A. (1994). Simulating neural networks with mathematics. Addison-Wesley Publishing Company.
Friedman, M. (1968). The role of monetary policy. American Economic Review, 58(1), 1–17.
Fu, J. (1998). A neural network forecast of economic growth and recession. Journal of Economics, 24(1), 51–66.
García, P., Herrera, L. O., & Valdés, R. (2002). New frontiers for monetary policy in Chile. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges, Central Bank of Chile, Santiago, 627–649.
García, P., & Valdés, R. (2003). Dinero e inflación en el marco de metas de inflación. Economía Chilena, 6(1), 21–47.
Gately, E. (1996). Neural network for financial forecasting. John Wiley and Sons.
Goss, E. P., & Ramchandani, H. (1995). Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers. Journal of Economics and Finance, 19(3), 1–18. https://doi.org/10.1007/bf02920611
Granger, C. W. J., & Teräsvirta, T. (1993). Modelling nonlinear economic relationships. Oxford University PressOxford. https://doi.org/10.1093/oso/9780198773191.001.0001
Kasabov, N. (1996). Foundations of neural networks, fuzzy systems, and knowledge engineering. The MIT Press. https://doi.org/10.7551/mitpress/3071.001.0001
Kuan, C. M., & White, H. (1994). Artificial neural networks: An econometric perspective. Econometric Review, 13, 1–91.
Lim, G., & McNelis, P. D. (1998). The effect of the nikkei and the S&P on the all-Ordinaries: A comparison of three models. International Journal of Finance & Economics, 3(3), 217–228. https://doi.org/10.1002/(sici)1099-1158(199807)3:3<217::aid-ijfe78>3.0.co;2-1
Loayza, N., & Soto, R. (2002). Inflation targeting: An overview. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges, Central Bank of Chile, Santiago, 1–21.
Martin, V., Choo, E., & Teo, L. (1997). Exchange rate forecasting models. In: Creedy, J. and V. Martin (eds.), Nonlinear Economic Models: Cross-Sectional, Time Series and Neural Network Applications. Cheltenham, UK, and Lyme, N.H.: Elgar.
McNelis, P. (1998). Money demand and seigniorage-Maximizing inflation in Chile: Approximation, learning, and estimation with neural networks. Revista de Análisis Económico, 13(2), 3–24.
Mishkin. F, K. (2002). A decade of inflation targeting in the world. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges. Central Bank of Chile, Santiago, 171–219.
Morandé, F. (2002). A decade of inflation targeting in Chile: Development, lessons and challenges. In: N. Loayza and R. Soto (eds.), Inflation Targeting: Design, Performance, Challenges, Central Bank of Chile, Santiago, 583–626.
Moshiri, S., Cameron, N. E., & Scuse, D. (1999). Static, dynamic, and hybrid neural networks in forecasting inflation. Computational Economics, 14(3), 219–235. https://doi.org/10.1023/a:1008752024721
Olmedo, I., & Fernández, E. (1997). Hybrid classifiers for financial multicriteria decisionMaking: The case of bankruptcy prediction. Computational Economics, 10(4), 317–35.
Qi, M. (1999). Nonlinear predictability of stock returns using financial and economic variables. Journal of Business & Economic Statistics, 17(4), 419–429. https://doi.org/10.1080/07350015.1999.10524830
Rosende, F. (1998). Política monetaria en Chile en los noventa. Un ejercicio no neutral. Perspectivas, 2(1), 157–189.
Rosende, F. (2001). La nueva síntesis keynesiana: Análisis e implicancias de política. Unpublished manuscript, November.
Rosende, F. (2003). ¿EL FIN DEL MONETARISMO? Cuadernos de economía, 40(121), 681–689. https://doi.org/10.4067/s0717-68212003012100040
Sargent, T. J. (1993). Bounded rationality in macroeconomics. Oxford University PressOxford. https://doi.org/10.1093/oso/9780198288640.001.0001
Shazly, M. R., & Shazly, H. E. (1999). Forecasting currency prices using a genetically evolved neural network architecture. International Review of Financial Analysis, 8(1), 67–82. https://doi.org/10.1016/s1057-5219(99)00006-x
Smith, P. A., & MacLin, O. H. (1995). HAVE PRESIDENTS INFLUENCED MONETARY POLICY: NEW EVIDENCE FROM AN ARTIFICIAL NEUTRAL NETWORK. Studies in Economics and Finance, 16(1), 23–45. https://doi.org/10.1108/eb028716
Soto, R. (1995). Non-Linearities in the demand for money: A neural network approach. Unpublished manuscript, Georgetown University.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195–214. https://doi.org/10.1016/0167-2231(93)90009-l
Tkacz, G., & Hu, S. (1999). Forecasting GDP using artificial neural networks. Working Paper Nº 99-3, Bank of Canada.
Vergara, R. (2003). EL DINERO COMO INDICADOR DE POLITICA MONETARIA EN CHILE. Cuadernos de economía, 40(121), 707–715. https://doi.org/10.4067/s0717-68212003012100043
White, H. (1992). Artificial neural networks: Approximation and learning. Blackwell.
Wilson, N., Chong, K. S., Peel, M. J., & Kolmogorov, A. N. (1995). Neural network simulation and the prediction of corporate outcomes: Some empirical findings. International Journal of the Economics of Business, 2(1), 31–50. https://doi.org/10.1080/758521095
Witkowska, D. (1999). Applying artificial neural networks to bank-decision simulations. International Advances in Economic Research, 5(3), 350–368. https://doi.org/10.1007/bf02296417
Woodward, R. (2000). Maestro: Greenspan’s fed and the American boom. Touchstone Books, Simon & Schuster.
Wu, C., & Wang, X. M. (2000). A neural network approach for analyzing small business lending decisions. Review of Quantitative Finance and Accounting, 15(3), 259–276. https://doi.org/10.1023/a:1008324023422
Yao, J., Tan, C. L., & Poh, H. L. (1999). NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI. International Journal of Theoretical and Applied Finance, 02(02), 221–241. https://doi.org/10.1142/s0219024999000145
Zurada, J. (1998). Neural networks versus logit regression models for predicting financial distress response variables. Journal of Applied Business Research, 15(1), 21–29.
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Copyright (c) 2005 Christian A. Johnson, Rodrigo Vergara

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