1. Advanced dynamic models Martin Ellison University of Warwick and CEPR Bank of England, December 2005

2. More complex models ImpulsesPropagationFluctuations Frisch-Slutsky paradigm

3. Shocks may be correlated Impulses Can add extra shocks to the model

4. Propagation Add lags to match dynamics of data (Del Negro-Schorfeide, Smets-Wouters) Taylor rule

5. Solution of complex models Blanchard-Kahn technique relies on invertibility of A 0 in state-space form.

6. QZ decomposition For models where A 0 is not invertible upper triangular QZ decomposition: s.t.

7. Recursive equations stable unstable Recursive structure means unstable equation can be solved first

8. Solution strategy Solve unstable transformed equation Translate back into original problem Substitute into stable transformed equation

9. Simulation possibilities Stylised facts Impulse response functions Forecast error variance decomposition

10. Optimised Taylor rule What are best values for parameters in Taylor rule ? Introduce an (ad hoc) objective function for policy

11. Brute force approach Try all possible combinations of Taylor rule parameters Check whether Blanchard-Kahn conditions are satisfied for each combination For each combination satisfying B-K condition, simulate and calculate variances

12. Brute force method Calculate simulated loss for each combination Best (optimal) coefficients are those satisfying B-K conditions and leading to smallest simulated loss

13. Grid search 012 2 1 For each point check B-K conditions Find lowest loss amongst points satisfying B-K condition

14. Next steps Ex 14: Analysis of model with 3 shocks Ex 15: Analysis of model with lags Ex 16: Optimisation of Taylor rule coefficients