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Sources of Uncertainty for Conducting Monetary Policy in Chile

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Author Info
Felipe Morandé Lavín
Mauricio Tejada

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Abstract

This paper analyzes the quantitative relevance of additive, multiplicative and data uncertainty in the implementation of Chile’s monetary policy. For the analysis of data uncertainty we focus on the uncertainty associated with the estimation of the output gap using real-time data and various well-known methods to estimate the output trend. We found that the revisions of the output gap are important and persistent and that the unobserved components method shows a better performance with real-time data than other more usual ones, like the HP filter. In the case of additive and multiplicative uncertainties we estimate the equations that govern the behavior of the economy with time-varying parameters and with state-dependent variances in the shocks of the model. This allows us to analyze the contribution of these two types of uncertainties on the total uncertainty. We found that additive uncertainty is the most relevant to explain total uncertainty and that shocks to the model are state-dependent.

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Paper provided by University of Chile, Department of Economics in its series Working Papers with number wp285.

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Length: 51 pages
Date of creation: May 2008
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Handle: RePEc:udc:wpaper:wp285

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Related research
Keywords: Real-time data; output gap; time-varying parameters models; models with regime changes; forward-looking models.;

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Find related papers by JEL classification:
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
E59 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Other
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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