1. 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

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

13. 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.

14. 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?

15. 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

16. Forecasts: USD/EUR @ 3 mo ahead

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