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

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

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  1. 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

  2. Outline • What have others tried • What is done in this paper • Should we expect time variation? • Different types of time variation • Additional variables, again

  3. 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

  4. 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

  5. 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

  6. 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.

  7. A Simple Example

  8. Present Value Relation

  9. Flashback: Rat-Ex/Systems Approach • Assume a stable AR(1) process for fundamentals • In principle, one could estimate this in a system

  10. BWB Setup

  11. 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

  12. 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.

  13. 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?

  14. 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

  15. Forecasts: USD/EUR @ 3 mo ahead

  16. Out-of-sample forecasting: USD/EUR

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