Narrow search
Showing 1 - 10 of 15
Cover Image
Dynamic Factor Trees and Forests : a theory-led machine learning framework for non-linear and state-dependent short-term U.S. GDP growth predictions
Wochner, Daniel - 2020 - This version: January 31, 2020
Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...
Persistent link: https://www.econbiz.de/10012172506
Cover Image
Forecasting Chinese GDP growth with mixed frequency data : which indicators to look at?
Mikosch, Heiner; Zhang, Ying - 2014 - First version: December 01, 2013, This version: July 10, 2014
indicators for forecasting quarterly Chinese GDP growth. We iterate the evaluation over forecast horizons from 370 days to 1 day … at the respective day of forecast. This procedure allows us to detect how useful a specific indicator is at a specific … forecast horizon relative to other indicators. Despite being published with an (additional) lag of one month the OECD leading …
Persistent link: https://www.econbiz.de/10010376402
Cover Image
Dissecting the Purchasing Managers' Index : are all relevant components included? ; are all included components relevant?
Siliverstovs, Boriss - 2015
We apply the novel approach of Siliverstovs (2015) to modelling data sampled at different frequencies in order to scrutinise the composition of one of the most influential economic indicators in Switzerland. The Purchasing Managers' Index consists of eight sub-indices out of which only five...
Persistent link: https://www.econbiz.de/10010498418
Cover Image
The franc shock and Swiss GDP : how long does it take to start feeling the pain?
Siliverstovs, Boriss - 2015
The paper addresses the question on what is the typical time horizon over which a full transmission of movements in the real exchange rate into real economy takes place. To this end, we base our analysis on the mixed-frequency small-scale dynamic factor model of Siliverstovs (2012) fitted to the...
Persistent link: https://www.econbiz.de/10010482019
Cover Image
Dynamic Factor Trees and Forests : a theory-led machine learning framework for non-linear and state-dependent short-term U.S. GDP growth predictions
Ehmann, Daniel - 2022 - Updated version
Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...
Persistent link: https://www.econbiz.de/10013187309
Cover Image
Forecasting GDP growth from the outer space
Galimberti, Jaqueson K. - 2017
estimation uncertainty affecting the country-specific estimates tends to be more pronounced for low and lower middle income …
Persistent link: https://www.econbiz.de/10011619456
Cover Image
The Swiss disease : facts and artefacts
Abrahamsen, Yngve; Aeppli, Roland; Atukeren, Erdal; … - 2003 - [Elektronische Ressource]
In their introduction to the special issue of the Review of Economic Dynamics on "Great Depressions of the 20th Century" (Vol. 5, 2002), Timothy J. Kehoe and Edward C. Prescott argue that in the last few years "great depressions" have hit two rich countries: New Zealand and Switzerland. We...
Persistent link: https://www.econbiz.de/10001767731
Cover Image
Finanzielle Entwicklung und wirtschaftliches Wachstum : eine Mehrwellen-Pfadanalyse
Graff, Michael (contributor) - 2002 - [Elektronische Ressource]
Persistent link: https://www.econbiz.de/10001767793
Cover Image
Hochschulbildung und Wirtschaftswachstum : neue empirische Ergebnisse
Graff, Michael (contributor) - 2002 - [Elektronische Ressource]
Persistent link: https://www.econbiz.de/10001767799
Cover Image
Socio-economic factors and the finance-growth nexus
Graff, Michael (contributor) - 2002 - [Elektronische Ressource]
Persistent link: https://www.econbiz.de/10001744762