| pdf-Format:
|
|
|
| Gedruckte Ausgabe: |
|
|
| Dokument in Google Scholar suchen: |
|  
|
| Social Media: |
|
|
| Export: |
|
|
| Abrufstatistik: |
| 
|
| SWD-Schlagwörter: |
| Eurozone , Bruttoinlandsprodukt |
| Freie Schlagwörter (Englisch): |
| Density Forecast Combination and Evaluation , Mixed–Frequency Data , Dynamic Factor Models , State Space Models |
| Institut: |
| Institut für Volkswirtschaftslehre |
| DDC-Sachgruppe: |
| Wirtschaft |
| Dokumentart: |
| ResearchPaper |
| Schriftenreihe: |
| Hohenheim discussion papers in business, economics and social sciences |
| Bandnummer: |
| 2015,03 |
| Sprache: |
| Englisch |
| Erstellungsjahr: |
| 2015 |
| Publikationsdatum: |
| 09.04.2015 |
| |
| Lizenz: |
|
Veröffentlichungsvertrag mit der Universitätsbibliothek Hohenheim
|
| |
| Kurzfassung auf Englisch: |
| EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged–edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules. |
Print-on-Demand-Kopie
