|
000
| 02910nam 2200445zi 4500 |
|---|
| 001 | 9.932256 |
|---|
| 003 | CaOODSP |
|---|
| 005 | 20240110085712 |
|---|
| 006 | m o d f |
|---|
| 007 | cr mn||||||||| |
|---|
| 008 | 240110t20232023onca ob f000 0 eng d |
|---|
| 040 | |aCaOODSP|beng|erda|cCaOODSP |
|---|
| 041 | 0 |aeng|beng|bfre |
|---|
| 043 | |ae------|an-us--- |
|---|
| 086 | 1 |aFB3-5/2023-61E-PDF |
|---|
| 100 | 1 |aChernis, Tony, |eauthor. |
|---|
| 245 | 10|aPredictive density combination using a tree-based synthesis function / |cby Tony Chernis, Niko Hauzenberger, Florian Huber, Gary Koop and James Mitchell. |
|---|
| 264 | 1|a[Ottawa] : |bBank of Canada = Banque du Canada, |c2023. |
|---|
| 264 | 4|c©2023 |
|---|
| 300 | |a1 online resource (ii, 36, 19 pages) : |billustrations (chiefly colour). |
|---|
| 336 | |atext|btxt|2rdacontent |
|---|
| 337 | |acomputer|bc|2rdamedia |
|---|
| 338 | |aonline resource|bcr|2rdacarrier |
|---|
| 490 | 1 |aStaff working paper = Document de travail du personnel, |y1701-9397 ; |v2023-61 |
|---|
| 500 | |aISSN assigned to different series. |
|---|
| 500 | |a"Last updated: December 28, 2023." |
|---|
| 504 | |aIncludes bibliographical references (pages 34-36). |
|---|
| 520 | 3 |a"Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area's Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits—in terms of improved forecast accuracy and interpretability—of modeling the synthesis function nonparametrically"--Abstract, page ii. |
|---|
| 546 | |aIncludes abstracts in English and French. |
|---|
| 650 | 0|aGross domestic product|zEuropean Union countries|xForecasting|xEconometric models. |
|---|
| 650 | 0|aInflation (Finance)|zUnited States|xForecasting|xEconometric models. |
|---|
| 650 | 0|aDecision trees. |
|---|
| 650 | 0|aRegression analysis. |
|---|
| 650 | 6|aProduit intérieur brut|zPays de l'Union européenne|xPrévision|xModèles économétriques. |
|---|
| 650 | 6|aInflation|zÉtats-Unis|xPrévision|xModèles économétriques. |
|---|
| 650 | 6|aArbres de décision. |
|---|
| 650 | 6|aAnalyse de régression. |
|---|
| 710 | 2 |aBank of Canada, |eissuing body. |
|---|
| 830 | #0|aStaff working paper (Bank of Canada)|v2023-61.|w(CaOODSP)9.806221 |
|---|
| 856 | 40|qPDF|s1.04 MB|uhttps://publications.gc.ca/collections/collection_2024/banque-bank-canada/FB3-5-2023-61-eng.pdf |
|---|