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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
Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and … proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which … powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting …
Persistent link: https://www.econbiz.de/10012546027
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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
Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and … proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which … powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting …
Persistent link: https://www.econbiz.de/10012172506
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Comprehensive Benchmark Revisions for The Conference Board Leading Economic Index® for the United States
Ozyildirim, Ataman - 2012
based on Markov Switching models. Real time out-of-sample forecasting exercises are used to confirm that the changes to the …
Persistent link: https://www.econbiz.de/10013112924
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Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath : A Review and Suggestions for the U.S. Federal Government
Greenberg, Michael R. - 2008
The number and magnitude of devastating natural and human events make it imperative that we actively and systematically estimate the costs and benefits of policy decisions in affected localities, regions, states, and nations. Such strategic risk management preparedness efforts should forecast...
Persistent link: https://www.econbiz.de/10012772101
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Support vector regression based GARCH model with application to forecasting volatility of financial returns
Chen, Shiyi (contributor); Jeong, Kiho (contributor);  … - 2008
financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …
Persistent link: https://www.econbiz.de/10003636113
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Nonlinear time series and neural-network models of exchange rates between the US Dollar and major currencies
Allen, David E.; McAleer, Michael; Peiris, Shelton; … - 2015
This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models,...
Persistent link: https://www.econbiz.de/10011378229
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Nonlinear time series and neural-network models of echange rates between the US Dollar and major currencies
Allen, David E.; McAleer, Michael; Peiris, Shelton; … - In: Risks : open access journal 4 (2016) 1, pp. 1-14
This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models,...
Persistent link: https://www.econbiz.de/10011443686
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Distributional Forests for Short-Term Density Predictions of U.S. Inflation
Wochner, Daniel - 2020
Recent work by Medeiros et al. (2019, Journal of Business & Economic Statistics) shows that point forecasts of the random forest machine learning algorithm systematically outperform well-established benchmarks at predicting U.S. inflation. This article extends their work from point to density...
Persistent link: https://www.econbiz.de/10012834887
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The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate : A Nonlinear Approach
Gogas, Periklis - 2017
inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine … learning and econometric methodologies in forecasting the U.S. inflation based on autoregressive and structural models of the … forecasting considering the term–spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871 …
Persistent link: https://www.econbiz.de/10012953784
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Revenue Impacts of the 2015 Avian Influenza Virus Outbreak on the United States Table Egg Wholesalers
Paukett, Michelle R. - 2018
From December 2014 to June 2015, the U.S. poultry industry experienced an outbreak of highly pathogenic avian influenza (AI), resulting in considerable bird depopulations. Both turkey and egg producers were impacted and farms affected faced losses from costs of bird disposal and farm...
Persistent link: https://www.econbiz.de/10012918198