Forecasting World Trade Using Big Data and Machine Learning Techniques
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DOI: 10.34932/01mq-sn15
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- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010.
"Large Bayesian vector auto regressions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
- Reichlin, Lucrezia & Giannone, Domenico & Banbura, Marta, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
- Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
- Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta, 2008. "Large Bayesian VARs," Working Paper Series 966, European Central Bank.
- Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016.
"The forecast combination puzzle: A simple theoretical explanation,"
International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
- Gerda Claeskens & Jan Magnus & Andrey Vasnev & Wendun Wang, 2014. "The Forecast Combination Puzzle: A Simple Theoretical Explanation," Tinbergen Institute Discussion Papers 14-127/III, Tinbergen Institute.
- Gerda Claeskens & Jan Magnus & Andrey Vasnev & Wendun Wang, 2016. "The forecast combination puzzle: a simple theoretical explanation," Working Papers of Department of Decision Sciences and Information Management, Leuven 532152, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
- Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021.
"Nowcasting GDP using machine-learning algorithms: A real-time assessment,"
International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
- Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series DP2019/03, Reserve Bank of New Zealand.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015.
"Prior Selection for Vector Autoregressions,"
The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," NBER Working Papers 18467, National Bureau of Economic Research, Inc.
- Domenico Giannone & Michèle Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," Working Papers ECARES ECARES 2012-002, ULB -- Universite Libre de Bruxelles.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2012. "Prior Selection for Vector Autoregressions," CEPR Discussion Papers 8755, C.E.P.R. Discussion Papers.
- Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- David F. Hendry & Michael P. Clements, 2004.
"Pooling of forecasts,"
Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
- David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
- Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
- Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
- Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021.
"Nowcasting GDP using machine-learning algorithms: A real-time assessment,"
International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
- Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
- Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2018. "Nowcasting New Zealand GDP using machine learning algorithms," CAMA Working Papers 2018-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018.
"Macroeconomic Nowcasting and Forecasting with Big Data,"
Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2017. "Macroeconomic nowcasting and forecasting with big data," Staff Reports 830, Federal Reserve Bank of New York.
- Giannone, Domenico & Tambalotti, Andrea & Sbordone, Argia & Bok, Brandyn & Caratelli, Daniele, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," CEPR Discussion Papers 12589, C.E.P.R. Discussion Papers.
- Soohyon Kim, 2020. "Macroeconomic and Financial Market Analyses and Predictions through Deep Learning," Working Papers 2020-18, Economic Research Institute, Bank of Korea.
- Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010.
"Large Bayesian vector auto regressions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
- Marta Bańbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92, January.
- George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
- Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
- repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
- Sims, Christopher A & Zha, Tao, 1998.
"Bayesian Methods for Dynamic Multivariate Models,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
- Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," FRB Atlanta Working Paper 96-13, Federal Reserve Bank of Atlanta.
- Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
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More about this item
JEL classification:
- F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2022-10-17 (Big Data)
- NEP-CMP-2022-10-17 (Computational Economics)
- NEP-FOR-2022-10-17 (Forecasting)
- NEP-INT-2022-10-17 (International Trade)
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