A small quarterly multi-country projection model /

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Bibliographic Details
Imprint:Washington, D.C. : International Monetary Fund, ©2008.
Description:1 online resource (59 pages) : illustrations
Language:English
Series:IMF working paper ; WP/08/279.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/14153405
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Other authors / contributors:Carabenciov, Ioan, author.
International Monetary Fund. Research Department.
ISBN:1462324509
9781462324507
1452737975
9781452737973
1283516373
9781283516372
9786613828828
6613828823
1451989261
9781451989267
Notes:At head of title: Research Dept.
"December 2008."
Includes bibliographical references (pages 31-32).
Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2011.
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212
English.
digitized 2011 HathiTrust Digital Library committed to preserve
Print version record.
Summary:This is the second 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. The model is estimated with Bayesian techniques, which provide a very efficient way of imposing restrictions to produce both plausible dynamics and sensible forecasting properties. We show how the model can be used to construct efficient baseline forecasts that incorporate judgment imposed on the near-term outlook.
Other form:Print version: Small quarterly multi-country projection model. Washington, D.C. : International Monetary Fund, ©2008
Table of Contents:
  • I. Introduction; II. Benchmark Model; A. Background; B. The Specification of The Model; B.1. Observable variables and data definitions; B.2. Stochastic processes and model definitions; B.3. Behavioral equations; B.4. Cross correlations of disturbances; III. Extending the Model to Include Financial-Real Linkages; A. Background; B. Model Specification Incorporating the US Bank Lending Tightening Variable; IV. Confronting The Model with The Data; A. Bayesian Estimation; B. Results; B.1. Estimates of coefficients.
  • B.2. Estimates of standard deviation of structural shocks and cross correlationsB. 3. RMSEs; B.4. Impulse response functions; C. Forecasting with Bayesian Estimates; V. Concluding Remarks; References; Appendix; GPM Data Definitions; Figures; 1. Comparison between Output GAP and BLT indicator; 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 Errors2. Demand shock in the US (1); 3. Demand shock in the US (2); 4. Demand shock in the US (3); 5. Demand shock in Europe (1); 6. Demand shock in Europe (2); 7. Demand shock in Europe (3); 8. Demand shock in Japan (1); 9. Demand shock in Japan (2); 10. Demand shock in Japan (3); 11. Financial (BLT) shock in the US (1); 12. Financial (BLT) shock in the US (2); 13. Financial (BLT) shock in the US (3); 14. Growth rate shock in the US (1); 15. Growth rate shock in the US (2); 16. Growth rate shock in the US (3); 17. Forecast Results (1); 18. Forecast Results (2).