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Learning about monetary policy rules when the housing market matters

  • Xiao, Wei
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In this paper we study a general equilibrium model with a housing market, and use stability under adaptive learning as a criterion to evaluate monetary policy rules. An important feature of the model is that there exist credit-constrained borrowers who use their housing assets as collateral to finance purchases. We evaluate both conventional Taylor rules and rules that incorporate other targets such as housing prices. We find that the effect of responding to housing prices, in addition to output and inflation, depends critically on the assumed information structure of the economy.

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Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 37 (2013)
Issue (Month): 3 ()
Pages: 500-515

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Handle: RePEc:eee:dyncon:v:37:y:2013:i:3:p:500-515
Contact details of provider: Web page: http://www.elsevier.com/locate/jedc

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  7. John B. Taylor, 2007. "Housing and monetary policy," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 463-476.
  8. Evans, George W. & McGough, Bruce, 2003. "Monetary policy, indeterminacy and learning," CFS Working Paper Series 2003/37, Center for Financial Studies (CFS).
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  18. Seppo Honkapohja & Kaushik Mitra, 2006. "Learning Stability in Economies with Heterogeneous Agents," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 9(2), pages 284-309, April.
  19. Bullard, James & Mitra, Kaushik, 2002. "Learning about monetary policy rules," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1105-1129, September.
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  22. James Bullard & Eric Schaling, 2009. "Monetary Policy, Determinacy, and Learnability in a Two-Block World Economy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(8), pages 1585-1612, December.
  23. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
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