I. Introduction; II. Benchmark Model; A. Background; B. The Specification of the Model; B.1. Observable variable and data definitios; B.2. Stochastic processes and model definitions; B.3 Behavioral equations; B.4. Cross corelations of disturbances; III. Extending the Model to Include Financial-Real Linkages; A. Background; B. Model Specification Incorporating the US Bank Lending Tightening Variable; IV. Extending the Model to Include Oil Prices; A. Background; B. Model Specification Incorporating Oil Price; V. Confronting The Model with The Data; A. Bayesian Estimation; B. Results
B.1. Estimates of coefficientsB. 2. Estimates of standard deviation of structural shocks and cross correlations; B.3. RMSEs; B.4. Impulse response functions; C. Forecasting with Bayesian Estimates; VI. Concluding Remarks; References; Appendix: GPM Data Definitions; Figures; 1. Comparison between Output Gap and BLT indicator; 2. Real Oil Price; Tables; 1. Results from posterior maximization (1); 2. Results from posterior maximization (2); 3. Results from posterior maximization (3); 4. Results from posterior parameters (standard deviation of structural shocks)
5. Results from posterior parameters (correlation of structural Shocks)6. Root Mean Squared Errors; 3 Demand shock in the US (1); 4. Demand shock in the US (2); 5. Demand shock in the US (3); 6. Demand shock in Europe (1); 7. Demand shock in Europe (2); 8. Demand shock in Europe (3); 9. Demand shock in Japan (1); 10. Demand shock in Japan (2); 11. Demand shock in Japan (3); 12. Financial (BLT) shock in the US (1); 13. Financial (BLT) shock in the US (2); 14. Financial (BLT) shock in the US (3); 15. Growth rate shock in the US (1); 16. Growth rate shock in the US (2)
This is the third of a series of papers that are being written as part of a larger project to estimate a small quarterly Global Projection Model (GPM). The GPM project is designed to improve the toolkit for studying both own-country and cross-country linkages. In this paper, we estimate a small quarterly projection model of the US, Euro Area, and Japanese economies that incorporates oil prices and allows us to trace out the effects of shocks to oil prices. The model is estimated with Bayesian techniques. We show how the model can be used to construct efficient baseline forecasts that incorporate judgment imposed on the near-term outlook
Notes
At head of title: Research Dept
"December 2008."
Bibliography
Includes bibliographical references (pages 36-38)
Notes
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