1. 1 Conditional Forecasts in DSGE models Author: Junior Maih Discussant: Alon Binyamini Central Bank Macroeconomic Modeling Workshop 2009, Jerusalem

2. 2 Outline  Review: Motivation Technique Main results  Comments on: Technique Implementation Conclusion Text

3. 3 Review

4. 4 Review – motivation  Background – improving DSGE forecast by conditioning information. DSGE offer interpretation. DSGE are forward looking – future info is relevant and useful. Yet, may fail to forecast some (central) endogenous variables.  Questions: How to carry out (soft) conditional forecast? When hard is superior to soft condition?

5. 5 Review – contributions The Junior Smoother  Technique for soft conditional forecast with fw-looking DSGE Nests hard and unconditional. Deals with two source of uncertainty  (present and future) state uncertainty.  Structural uncertainty.  Extract the most likely shocks-combination that satisfies the conditioning restrictions. E.g. : conditioning on future interest rate … The spirit of Kalman smoother.  Extension of earlier contribution - Waggoner & Zha (1999).  Utilized to show that relevant information can be too tight.

6. 6 Comments

7. 7 Comment on the methodology  Comment: Backcasting doesn’t employ the conditioning information.  Back-casting is inefficient.  Starting values are inconsistent with conditioning information. Not an issue for unconditional forecast.  Suggestion – iterate till convergence: Compute conditional forecast using the Junior smoother. Extend sample by forecast. Update back-casting by Kalman smoother. Now, starting values for forecast may differ. So, repeat to convergence.

8. 8 Comment on the implementation  Verdict: Conditioning doesn ’ t necessarily improve forecast: Relevant information can be too tight. Disappointing result attributed to misspecification.  But, Unconditional forecast is also misspecified. Ex-post realizations vis-à-vis ex-ante expectations.  Two suggestions Repeat the analysis with ex-ante expectations. Divide and conquer by Monte-Carlo simulation:  Forecast with “true” DGP (misspecification or irrelevant conditioning?).  Distinguish between state and structural uncertainties.

9. 9 Comparison with Kalman Smoother If not equivalent, where the does the difference come from?  Is it equivalent to KS with conditioning restrictions as observables and nowcast for initialization?  “…allows for the possibility of agents reacting to anticipated future events beyond one step ahead.” But this should be attributed to the state-space representation:  “…it does not change the initial conditions of the state vector.” Can restrict the KS similarly. But, is it efficient? Keynes: “When the facts change, I change my mind. What do you do, sir?”

10. 10 Comments on the text (cont’)  Under similar treatment, would KS & JS extract similar or different shocks? Why?  Chapter 2 (the intuition-building example) Why tighter conditioning shifts expectations without increasing certainty?

11. 11 To conclude  Two birds: Interesting. Useful.  Appealing the disappointing verdict: Relevant and reliable hard conditioning might turn out to be too tight. But, ex-post realizations may not be relevant. Even if correct, misspecification might be the wrong guy to blame.

12. 12 The End ! Thank you

13. 13 The Bivariate normal example