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Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)

   | 12. Sept. 2022

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Amidi, A., and S. Amidi. 2019. “Recurrent Neural Networks Cheatsheet”. Recurrent Neural Networks Cheatsheet. 2019. Available at: https://stanford.edu/,shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks. (accessed December 2020). Search in Google Scholar

Antolin-Diaz, J., T. Drechsel, and I. Petrella. 2020. Advances in Nowcasting Economic Activity: Secular Trends, Large Shocks and New Data. DOI: http://dx.doi.org/10.2139/ssrn.3669854.10.2139/ssrn.3669854 Search in Google Scholar

Banbura, M., D. Giannone, and L. Reichlin. 2010. Nowcasting. ECB Working Paper 1275. DOI: http://dx.doi.org/10.2139/ssrn.1717887.10.2139/ssrn.1717887 Search in Google Scholar

Bańbura, M, and G. Rünstler. 2011. “A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP”. International Journal of Forecasting 27 (2): 333–346. DOI: https://doi.org/10.1016/j.ijforecast.2010.01.011. Search in Google Scholar

Bok, B., D. Caratelli, D. Giannone, A.M. Sbordone, and A. Tambalotti. 2018. “Macroeconomic Nowcasting and Forecasting with Big Data”. Annual Review of Economics 10 (1): 615–643. DOI: https://doi.org/10.1146/annurev-economics-080217-053214. Search in Google Scholar

Brownlee, J. 2018. Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery. Available at: https://books.google.ch/books?id=o5qnDwAAQBAJ. Search in Google Scholar

Buono, D., G. Mazzi, M. Marcellino, and Kapetanios. 2017. “Big Data Types for Macroeconomic Nowcasting”. Eurona. Search in Google Scholar

Camacho, M., Y. Lovcha, and G. Perez Quiros. 2015. “Can We Use Seasonally Adjusted Variables in Dynamic Factor Models?” Studies in Nonlinear Dynamics & Econometrics 19 (3): 377–391. DOI: https://doi.org/doi:10.1515/snde-2013-0096.10.1515/snde-2013-0096 Search in Google Scholar

Cantú, F. 2018. Estimation of a Coincident Indicator for International Trade and Global Economic Activity. 27. UNCTAD Research Paper. UNCTAD. Available at: https://unctad.org/system/files/official-document/ser-rp-2018d9_en.pdf. Search in Google Scholar

Chernis, T., and R. Sekkel. 2017. “A Dynamic Factor Model for Nowcasting Canadian GDP Growth”. Empirical Economics 53(1): 217–234. DOI: https://doi.org/10.1007/s00181-017-1254-1. Search in Google Scholar

Chung, J.,Ç. Gülçehre, K. Cho, and Y. Bengio. 2014. “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling”. CoRR abs/1412.3555. Available at: http://arxiv.org/abs/1412.3555. Search in Google Scholar

De Veaux, R.D., and L.H. Ungar. 1994. “Multicollinearity: A Tale of Two Nonparametric Regressions”. In Selecting Models from Data, edited by P. Cheeseman and R.W. Oldford, 393–402. New York, NY: Springer New York.10.1007/978-1-4612-2660-4_40 Search in Google Scholar

Dematos, G., M.S. Boyd, B. Kermanshahi, N. Kohzadi, and I. Kaastra. 1996. “Feedforward versus Recurrent Neural Networks for Forecasting Monthly Japanese Yen Exchange Rates”. Financial Engineering and the Japanese Markets 3(1): 59–75. DOI: https://doi.org/10.1007/BF00868008. Search in Google Scholar

Domo. 2017. “Data Never Sleeps 5.0”. Data Never Sleeps 5.0. 2017. Available: https://www.domo.com/learn/data-never-sleeps-5. (accessed September 2021). Search in Google Scholar

Doz, C., D. Giannone, and L. Reichlin. 2011. “A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering”. Journal of Econometrics 164(1): 188–205. DOI: https://doi.org/10.1016/j.jeconom.2011.02.012. Search in Google Scholar

Einav, L., and J. Levin. 2014. “The Data Revolution and Economic Analysis”. Innovation Policy and the Economy 14: 1–24. DOI: https://doi.org/10.1086/674019. Search in Google Scholar

Engle, R.F. 1982. “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”. Econometrica 50(4): 987–1007. DOI: https://doi.org/10.2307/1912773 Search in Google Scholar

Ennett, C.M., M. Frize, and C.R. Walker. 2001. “Influence of Missing Values on Artificial Neural Network Performance.” Studies in Health Technology and Informatics 84 (1): 449–53. Search in Google Scholar

Falat, L., and L. Pancikova. 2015. “Quantitative Modelling in Economics with Advanced Artificial Neural Networks”. Procedia Economics and Finance 34: 194–201. DOI: https://doi.org/10.1016/S2212-5671(15)01619-6. Search in Google Scholar

Fan, F., J. Xiong, and G. Wang. 2020. “On Interpretability of Artificial Neural Networks”. CoRR abs/2001.02522. Available at: http://arxiv.org/abs/2001.02522. Search in Google Scholar

Federal Reserve Bank of New York. 2021. “Nowcasting Report: Methodology”. Nowcasting Report: Methodology. 2021. Available at: https://www.newyorkfed.org/research/policy/nowcast/methodology.html. (accessed November 2020) Search in Google Scholar

Giannone, D., L. Reichlin, and S. Simonelli. 2009. “Nowcasting Euro Area Economic Activity in Real Time: The Role of Confidence Indicators”. National Institute Economic Review 210 (1): 90–97. DOI: https://doi.org/10.1177/0027950109354413. Search in Google Scholar

Giannone, D., L. Reichlin, and D. Small. 2005. Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases. Centre for Economic Policy Research. Available et: https://cepr.org/active/publications/discussion_papers/dp.php?dpno=5178.10.2139/ssrn.873658 Search in Google Scholar

Giannone, D., L. Reichlin, and D. Small. 2008. “Nowcasting: The Real-Time Informational Content of Macroeconomic Data”. Journal of Monetary Economics 55(4): 665–666. DOI: https://doi.org/10.1016/j.jmoneco.2008.05.010. Search in Google Scholar

Grosse, R. 2017. Lecture 15: Exploding and Vanishing Gradients. Available at: http://www.cs.toronto.edu/,rgrosse/courses/csc321_2017/readings/L15%20Exploding%20and%20Vanishing%20Gradients.pdf. (accessed December 2020) Search in Google Scholar

Guichard, S., and E. Rusticelli. 2011. A Dynamic Factor Model for World Trade Growth: 874.OECD Economics Department Working Papers. DOI: https://doi.org/10.1787/5kg9zbvvwqq2-en. Search in Google Scholar

Gurney, K. 1997. An Introduction to Neural Networks. USA: Taylor & Francis, Inc..10.4324/9780203451519 Search in Google Scholar

Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory”. Neural Computation 9: 1735–80. DOI: https://doi.org/10.1162/neco.1997.9.8.1735.9377276 Search in Google Scholar

Hodas, N.O., and P. Stinis. 2018. “Doing the Impossible: Why Neural Networks Can Be Trained at All”. Frontiers in Psychology 9: 1185. DOI: https://doi.org/10.3389/fpsyg.2018.01185.605212530050485 Search in Google Scholar

Hopp, D. 2021a. “nowcast_lstm”. Available at: https://github.com/dhopp1/nowcast_lstm/ Search in Google Scholar

Hopp, D. 2021b. “nowcastLSTM”. Available at: https://github.com/dhopp1/nowcastLSTM/ Search in Google Scholar

Hopp, D. 2021c. “nowcast_lstm_matlab”. Available at: https://github.com/dhopp1/nowcast_lstm_matlab/ Search in Google Scholar

Hopp, D. 2021d. “NowcastLSTM.jl”. Available at: https://github.com/dhopp1/NowcastLSTM.jl/ Search in Google Scholar

Johansen, S. 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. New York.10.1093/0198774508.001.0001 Search in Google Scholar

Keskar, N.S., D. Mudigere, J. Nocedal, M. Smelyanskiy, and P.T.P. Tang. 2017. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. Available at: https://arxiv.org/abs/1609.04836. Search in Google Scholar

Kozlov, M., S. Karaivanov, D. Tsonev, and R. Valkov. 2018. “The News on Norcasting”. The News on Nowcasting. Available at: https://www.weareworldquant.com/en/thought-leadership/the-news-on-nowcasting/. (accessed December 2020) Search in Google Scholar

Kurihara, Y., and A. Fukushima. 2019. “AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries”. Applied Economics and Finance 6: 1. DOI: https://doi.org/10.11114/aef.v6i3.4126. Search in Google Scholar

Kuzin, V.N., M. Marcellino, and C. Schumacher. 2009. MIDAS versus Mixed-Frequency VAR: Nowcasting GDP in the Euro Area. Discussion Paper Series 1: Economic Studies 2009,07. Deutsche Bundesbank. Available at: https://ideas.repec.org/p/zbw/bubdp1/7576.html.10.2139/ssrn.2785336 Search in Google Scholar

Loermann, J., and B. Maas. 2019. Nowcasting US GDP with Artificial Neural Networks. MPRA Paper 95459. University Library of Munich, Germany. Available at: https://ideas.repec.org/p/pra/mprapa/95459.html. Search in Google Scholar

MacFeely, S. 2020. “In Search of the Data Revolution: Has the Official Statistics Paradigm Shifted?” Statistical Journal of the IAOS 36(4): 1075–1094. DOI: https://doi.org/10.3233/SJI-200662. Search in Google Scholar

Marcellino, M., and C. Schumacher. 2010. “Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP”. Oxford Bulletin of Economics and Statistics 72(4): 518–550.10.1111/j.1468-0084.2010.00591.x Search in Google Scholar

Mariano, R.S., and Y. Murasawa. 2003. “A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series”. Journal of Applied Econometrics 18(4): 427–443. DOI: https://doi.org/10.1002/jae.695. Search in Google Scholar

Matheson, T. 2011. “New Indicators for Tracking Growth in Real Time”. IMF Working Paper 11 (43): 1–22.10.5089/9781455218998.001 Search in Google Scholar

Molnar, C. 2019. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Search in Google Scholar

Montavon, G., W. Samek, and K.-R. Müller. 2018. “Methods for Interpreting and Understanding Deep Neural Networks”. Digital Signal Processing 73: 1–15. DOI: https://doi.org/10.1016/j.dsp.2017.10.011. Search in Google Scholar

Morgado, A.J., L. Catela Nunes, and S. Salvado. 2007. Nowcasting an Economic Aggregate with Disaggregate Dynamic Factors: An Application to Portuguese GDP. GEE Papers 0002. Gabinete de Estratégia e Estudos, Ministério da Economia. Available at: https://ideas.repec.org/p/mde/wpaper/0002.html. Search in Google Scholar

Nielsen, M.A. 2015. “Neural Networks and Deep Learning.” Determination Press. Search in Google Scholar

Olah, C. 2015. Understanding LSTM Networks. Available at: https://colah.github.io/-posts/2015-08-Understanding-LSTMs/. Search in Google Scholar

OpenTable. 2021. “The State of the Restaurant Industry”. The State of the Restaurant Industry. Available at: https://www.opentable.com/state-of-industry. (accessed September 2021). Search in Google Scholar

Porshakov, A., A. Ponomarenko, and A. Sinyakov. 2016. “Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model”. Journal of the New Economic Association 30(2): 60–76.10.31737/2221-2264-2016-30-2-3 Search in Google Scholar

PyTorch. 2021a. “Dropout”. Available at: https://pytorch.org/docs/stable/generated/-torch.nn.Dropout.html. (accessed October 2021) Search in Google Scholar

PyTorch. 2021b. “Loss Functions”. Available at: https://pytorch.org/docs/stable/nn.html#loss-functions. (accessed August 2021). Search in Google Scholar

PyTorch. 2021c. “LSTM”. Available at: https://pytorch.org/docs/stable/generated/-torch.nn.LSTM.html. (accessed October 2021). Search in Google Scholar

Rossiter, J. 2010. Nowcasting the Global Economy. 2010–2012. Bank of Canada. Available at: https://ssrn.com/abstract=1674952. DOI: http://dx.doi.org/10.2139/ssrn.1674952.10.2139/ssrn.1674952 Search in Google Scholar

Rumelhart, D.E., G.E. Hinton, and R.J. Williams. 1986. “Learning Representations by Back-Propagating Errors”. Nature 323 (6088): 533–36. DOI: https://doi.org/10.1038/323533a0. Search in Google Scholar

Scikit-learn. 2021. “3.1. Cross-Validation: Evaluating Estimator Performance”. 3.1. Cross-Validation: Evaluating Estimator Performance. 2021. Availavle at: https://Scikit-learn.org/stable/modules/cross_validation.html. (accessed October 2021) Search in Google Scholar

Sharma, S., S. Sharma, and A. Athaiya. 2020. “Activation Functions in Neural Networks”. International Journal of Engineering Applied Sciences and Technology, 4(12): 310–316.10.33564/IJEAST.2020.v04i12.054 Search in Google Scholar

Shi, X., Z. Chen, H. Wang, D.-Y. Yeung, W. Wong, and W. Woo. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Search in Google Scholar

Smieja, M.Ł. Struski, J. Tabor, B. Zieliński, and P. Spurek. 2019. Processing of Missing Data by Neural Networks. Search in Google Scholar

Statista. 2021. “Coronavirus Impact on Retail E-Commerce Website Traffic Worldwide as of June 2020, by Average Monthly Visits”. Coronavirus Impact on Retail E-Commerce Website Traffic Worldwide as of June 2020, by Average Monthly Visits. 2021. Available at: https://www.statista.com/statistics/1112595/covid-19-impact-retail-e-commerce-site-traffic-global/. (accessed March 2021). Search in Google Scholar

Stock, J.H., and M.W. Watson. 2002. “Forecasting Using Principal Components From a Large Number of Predictors”. Journal of the American Statistical Association 97(460): 1167–1179. DOI: https://doi.org/10.11981016214502388618960.10.1198/016214502388618960 Search in Google Scholar

Stock, J.H., and M.W. Watson. 2004. “Combination Forecasts of Output Growth in a Seven-Country Data Set”. Journal of Forecasting 23(6): 405–430. DOI: https://doi.org/10.1002/for.928. Search in Google Scholar

Stratos, K. 2020. Feedforward and Recurrent Neural Networks. Available at: http://www1.cs.columbia.edu/,stratos/research/neural.pdf. (accessed December 2020) Search in Google Scholar

“Transforming Our World: The 2030 Agenda for Sustainable Development”. Transforming Our World: The 2030 Agenda for Sustainable Development. Available at:https://sdgs.un.org/2030agenda. Search in Google Scholar

UN. 2015. “Transforming Our World: The 2030 Agenda for Sustainable Development”. Transforming Our World: The 2030 Agenda for Sustainable Development. Available at: https://sdgs.un.org/2030agenda. (accessed September 2020). Search in Google Scholar

UNCTAD. 2020a. “UNCTADStat”. UNCTADStat. 2020. Available at: https://unctadstat.unctad.org/EN/Index.html. (accessed March 2021). Search in Google Scholar

UNCTAD. 2020b. “Global Merchandise Trade Nowcast December 2020”. Global Merchandise Trade Nowcast December 2020. Available at: https://unctad.org/system/files/official-document/gdsdsimisc2020d8_en.pdf. (accessed August 2020). Search in Google Scholar

UNSD. 2020. “Nowcasting and Forecasting for SDG Monitoring”. Presented at the Nowcasting and Forecasting for SDG Monitoring, February 3, Geneva, Switzerland. Available at: https://unstats.un.org/unsd/statcom/51st-session/side-events/20200302-2L-Nowcasting-and-Forecasting-for-SDG-Monitoring/. (accessed February 2021). Search in Google Scholar

“The X-13ARIMA-SEATS Seasonal Adjustment Program”. The X-13ARIMA-SEATS Seasonal Adjustment Program. 2017. Available at:https://www.census.gov/srd/www/-x13as/. Search in Google Scholar

USCB. 2017. “The X-13ARIMA-SEATS Seasonal Adjustment Program”. The X-13ARIMA-SEATS Seasonal Adjustment Program. 2017. Available at: https://www.census.gov/srd/www/x13as/. (accessed March 2021). Search in Google Scholar

WMO. 2017. “Guidelines for Nowcasting Techniques”. 1198. WMO. WMO. Available at: https://library.wmo.int/doc_num.php?explnum_id=3795. (accessed March 2021). Search in Google Scholar

WTO. 2020. “Statistics on Merchandise Trade”. Statistics on Merchandise Trade. 2020. Available at: https://www.wto.org/english/res_e/statis_e/merch_trade_stat_e.htm. (accessed November 2020). Search in Google Scholar

Yilmazkuday, Hakan. 2021. “Stay-at-Home Works to Fight against COVID-19: International Evidence from Google Mobility Data”. Journal of Human Behavior in the Social Environment 31 (1–4): 210–220. DOI: https://doi.org/10.1080/10911359.2020.1845903. Search in Google Scholar

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