Recursive Thick Modeling and the Choice of Monetary Policy in Mexico
AbstractBy following the spirit in Favero and Milani (2005), we use recursive thick modeling to take into account model uncertainty for the choice of optimal monetary policy. We consider an open economy model and generate multiple models for only the aggregate demand and aggregate supply. Models are constructed by matching the rankings of aggregate demand and aggregate supply and adding other specifications for the rest of the variables. The main results show that recursive thick modeling with equal and different weights approximates the recent historical behavior of nominal interest rates in Mexico better than recursive thin modeling
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 30.
Date of creation: 04 Jul 2006
Date of revision:
model uncertainty; optimal control; out-of-bag; thin modeling and thick modeling;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-15 (All new papers)
- NEP-CBA-2006-07-15 (Central Banking)
- NEP-MAC-2006-07-15 (Macroeconomics)
- NEP-MON-2006-07-15 (Monetary Economics)
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