Estimation of alternative monetary policy rules and their comparison
AbstractThe article shows the optimal monetary policy problem in three different cases. The first is optimization "under discretion". This means that a central bank can reoptimize its behaviour each period and is not history dependent. The second approach is optimization "under commitment" which means that a central bank makes a binding commitment and can't change its behaviour as a reaction to shocks. The last case is a simple Taylor rule which is not a result of optimization process, but demonstrates real behaviour of many central banks. The policy design problem is to characterize how the central bank should adjust the interest rate to the current state of the economy. The article shows the theoretical procedure of finding the optimal monetary policy. Further, it examines and illustrates the behaviour of the presented models on the Czech economy data. Model parameters are estimated simultaneously by the "Iterative Extended Kalman Filter Smoother". Impulse responses are tested. Fundamental differences of the three cases are explained and presented graphically. Results are economically interpreted.
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Bibliographic InfoArticle provided by The Czech Econometric Society in its journal Bulletin of the Czech Econometric Society.
Volume (Year): 11 (2004)
Issue (Month): 20 ()
monetary policy; forward-looking model; discretion; commitment; Taylor rule; Iterative Extended Kalman Filter Smoother; impulse responses;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
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