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Discussion of Can Parameter Instability Explain the Meese-Rogoff Puzzle by P. Bachetta, E. van Wincoop and T. Beutler - PowerPoint PPT Presentation


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Discussion of Can Parameter Instability Explain the Meese-Rogoff Puzzle? by P. Bachetta, E. van Wincoop and T. Beutler. Menzie D. Chinn UW-Madison and NBER. International Seminar on Macroeconomics Cyprus, June 12, 2009. Outline. What have others tried What is done in this paper

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Discussion ofCan Parameter Instability Explain theMeese-Rogoff Puzzle?by P. Bachetta, E. van Wincoop and T. Beutler

Menzie D. Chinn

UW-Madison and NBER

International Seminar on Macroeconomics

Cyprus, June 12, 2009


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Outline

  • What have others tried

  • What is done in this paper

  • Should we expect time variation?

  • Different types of time variation

  • Additional variables, again


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What Others Have Tried

  • Meese-Rogoff showed standard models could not outpredict a random walk in ex post historical simulations

  • This is not a necessary implication of market efficiency

  • Functional form (nonlinearity, thresholds)

  • Panel regressions

  • Regime switching

  • Additional variables


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The Paper

  • A radically different direction

  • Part of a research agenda (“scapegoat”, “unstable”)

  • Documents and reiterates the failure of structural macro models of exchange rates

  • Shows that appealing to parameter instability is unlikely to explain the Meese-Rogoff effects

  • Due to offsetting effects


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Time Varying Parameters?

  • Asset prices represent the present value of the fundamentals

  • If the variables can be expressed as stable autoregressive processes, then the current exchange rate is a function of current fundamentals (and autoregressive parameters)

  • If the variables follow a random walk, then the expression is very simple


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Time Varying Parameters?

  • But if the AR processes evolve, or change discretely, then the reduced form parameters change

  • And if the underlying structural relationships change, then the reduced form parameters change

  • Note: Stationary time varying parameters observationally equivalent to heteroskedasticity with time varying constant.




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Flashback: Rat-Ex/Systems Approach

  • Assume a stable AR(1) process for fundamentals

  • In principle, one could estimate this in a system



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Parameter Variation in BWB

  • Allows AR(1) in exchange rate equation

  • And AR(1) in fundamentals

  • And AR(1) in β’s

  • But holds constant the AR(1) processes

  • Finds that parameter variation cannot explain the Meese-Rogoff finding

  • Explanatory power is low explains MR


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Extensions

  • BWB examine a specific type of parameter variation.

  • They also try a Markov switching process

  • Obtain similar results

  • Observation: Like types of nonlinearities, there are infinite number of types of time variation.


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Perspective

  • The results can explain the MR results

  • But nihilistic to assert that fundamentals have little explanatory power

  • Why do we find so much evidence of cointegration between exchange rates and posited fundamentals?


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Adding in variables, again

  • An alternative is to look for the “magic” variable

  • Chinn-Moore argue for using order flow, which improves fit, in and out-of-sample

  • But in the sense that order flow is not a “fundamental”, this is a complementary, not competing, approach




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