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On the Estimation of Euler Equations in the Presence of a Potential Regime Shift

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  • Saikkonen, Pentti
  • Ripatti, Antti

Abstract

The concept of a peso problem is formalized in terms of a linear Euler equation and a non-linear marginal model describing the dynamics of the exogenous variable driving the process. It is shown that, using a threshold autoregressive model as a marginal model, it is possible to produce time-varying peso premia. A Monte Carlo method and a method based on the numerical solution of integral equations are considered as tools for computing conditional future expectations in the marginal model. A Monte Carlo study illustrates the poor performance of the generalized method of moments estimator in small and even relatively large samples. The poor performance is particularly acute in the presence of a peso problem but is also serious in the simple linear case. Copyright 2000 by Blackwell Publishers Ltd and The Victoria University of Manchester

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  • Saikkonen, Pentti & Ripatti, Antti, 2000. "On the Estimation of Euler Equations in the Presence of a Potential Regime Shift," Manchester School, University of Manchester, vol. 68(0), pages 92-121, Supplemen.
  • Handle: RePEc:bla:manchs:v:68:y:2000:i:0:p:92-121
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    References listed on IDEAS

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    1. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
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    3. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    5. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 423-425, October.
    6. Abel, Andrew B., 1982. "Dynamic effects of permanent and temporary tax policies in a q model of investment," Journal of Monetary Economics, Elsevier, vol. 9(3), pages 353-373.
    7. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
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    Cited by:

    1. Christian Julliard & Anisha Ghosh, 2012. "Can Rare Events Explain the Equity Premium Puzzle?," Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 3037-3076.
    2. Markku Lanne, 2003. "Testing the Expectations Hypothesis of the Term Structure of Interest Rates in the Presence of a Potential Regime Shift," Manchester School, University of Manchester, vol. 71(Supplemen), pages 54-67, September.
    3. Daniel G. Swaine, 2001. "Are taste and technology parameters stable? a test of "deep" parameter stability in real business cycle models of the U.S. economy," Working Papers 01-05, Federal Reserve Bank of Boston.
    4. Hing Chan & Kai Woo, 2006. "Bubbles detection for inter-war European hyperinflation: A threshold cointegration approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 30(2), pages 169-185, June.

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