Macroeconomic Indicator Forecasting with Deep Neural Networks
Download full text from publisher
Other versions of this item:
- Thomas Cook, 2019. "Macroeconomic Indicator Forecasting with Deep Neural Networks," 2019 Meeting Papers 402, Society for Economic Dynamics.
References listed on IDEAS
- Michael Creel, 2016. "Neural Nets for Indirect Inference," Working Papers 942, Barcelona Graduate School of Economics.
- Francis X. Diebold, 1998.
"The Past, Present, and Future of Macroeconomic Forecasting,"
Journal of Economic Perspectives,
American Economic Association, vol. 12(2), pages 175-192, Spring.
- Francis X. Diebold, 1997. "The Past, Present, and Future of Macroeconomic Forecasting," NBER Working Papers 6290, National Bureau of Economic Research, Inc.
- Francis X. Diebold, 1997. "The past, present, and future of macroeconomic forecasting," Working Papers 97-20, Federal Reserve Bank of Philadelphia.
- Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
- Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
- Pescatori, Andrea & Zaman, Saeed, 2011. "Macroeconomic models, forecasting, and policymaking," Economic Commentary, Federal Reserve Bank of Cleveland, issue Oct.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
- Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079, arXiv.org.
More about this item
KeywordsNeural networks; Forecasting; Macroeconomic indicators;
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-BIG-2018-01-22 (Big Data)
- NEP-CMP-2018-01-22 (Computational Economics)
- NEP-ETS-2018-01-22 (Econometric Time Series)
- NEP-FOR-2018-01-22 (Forecasting)
- NEP-MAC-2018-01-22 (Macroeconomics)
- NEP-ORE-2018-01-22 (Operations Research)
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedkrw:rwp17-11. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lu Dayrit). General contact details of provider: http://edirc.repec.org/data/frbkcus.html .