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A Likelihood-Based Evaluation of the Segmented Markets Friction in Equilibrium Monetary Models

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Listed author(s):
  • John Landon-Lane

    ()

    (Rutgers University)

  • Filippo Occhino

    ()

    (Rutgers University)

Abstract

This paper estimates and compares the full participation and the segmented markets monetary frameworks. In both models, the real sector and monetary policy determine exogenously the joint process for the aggregate endowment and the short-term nominal interest rate, while the money growth rate and the inflation rate are determined endogenously. Using linearized versions of the models, we use Bayesian methods to compare the two models over the full dimension of the data. This likelihood-based comparison overwhelmingly favors the segmented markets model over the full participation model. The estimate of the fraction of households participating in financial markets is approximately 13\%. The segmented markets model generates more persistent and more realistic impulse response functions to monetary policy shocks. Our results strongly suggest that taking the presence of market segmentation into account is important in understanding the short-run dynamics of the monetary sector.

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Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 200415.

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Length: 20 pages
Date of creation: 04 Aug 2004
Handle: RePEc:rut:rutres:200415
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Keywords: limited participation; segmented markets; Bayesian model comparison; monetary policy shocks;

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Find related papers by JEL classification:
  • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
  • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
  • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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  10. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
  11. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
  12. Lucas, Robert Jr., 1990. "Liquidity and interest rates," Journal of Economic Theory, Elsevier, vol. 50(2), pages 237-264, April.
  13. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
  14. Christopher A. Sims, 1996. "Macroeconomics and Methodology," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 105-120, Winter.
  15. DeJong, David N & Ingram, Beth Fisher & Whiteman, Charles H, 1996. "A Bayesian Approach to Calibration," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 1-9, January.
  16. Fuerst, Timothy S., 1992. "Liquidity, loanable funds, and real activity," Journal of Monetary Economics, Elsevier, vol. 29(1), pages 3-24, February.
  17. Fernandez-Villaverde, Jesus & Francisco Rubio-Ramirez, Juan, 2004. "Comparing dynamic equilibrium models to data: a Bayesian approach," Journal of Econometrics, Elsevier, vol. 123(1), pages 153-187, November.
  18. Filippo Occhino, 2004. "Modeling the Response of Money and Interest Rates to Monetary Policy Shocks: A Segmented Markets Approach," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(1), pages 181-197, January.
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Cited by:

  1. Mizrach, Bruce & Occhino, Filippo, 2008. "The impact of monetary policy on bond returns: A segmented markets approach," Journal of Economics and Business, Elsevier, vol. 60(6), pages 485-501.

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