References
- 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).
- 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
- 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
- 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.
- 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.
- 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.
- Buono, D., G. Mazzi, M. Marcellino, and Kapetanios. 2017. “Big Data Types for Macroeconomic Nowcasting”. Eurona.
- 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
- 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.
- 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.
- 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.
- 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
- 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.
- 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).
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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)
- 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.
- 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
- 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.
- 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)
- 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.
- Gurney, K. 1997. An Introduction to Neural Networks. USA: Taylor & Francis, Inc..10.4324/9780203451519
- 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
- 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
- Hopp, D. 2021a. “nowcast_lstm”. Available at: https://github.com/dhopp1/nowcast_lstm/
- Hopp, D. 2021b. “nowcastLSTM”. Available at: https://github.com/dhopp1/nowcastLSTM/
- Hopp, D. 2021c. “nowcast_lstm_matlab”. Available at: https://github.com/dhopp1/nowcast_lstm_matlab/
- Hopp, D. 2021d. “NowcastLSTM.jl”. Available at: https://github.com/dhopp1/NowcastLSTM.jl/
- Johansen, S. 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. New York.10.1093/0198774508.001.0001
- 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.
- 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)
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- Matheson, T. 2011. “New Indicators for Tracking Growth in Real Time”. IMF Working Paper 11 (43): 1–22.10.5089/9781455218998.001
- Molnar, C. 2019. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.
- 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.
- 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.
- Nielsen, M.A. 2015. “Neural Networks and Deep Learning.” Determination Press.
- Olah, C. 2015. Understanding LSTM Networks. Available at: https://colah.github.io/-posts/2015-08-Understanding-LSTMs/.
- 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).
- 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
- PyTorch. 2021a. “Dropout”. Available at: https://pytorch.org/docs/stable/generated/-torch.nn.Dropout.html. (accessed October 2021)
- PyTorch. 2021b. “Loss Functions”. Available at: https://pytorch.org/docs/stable/nn.html#loss-functions. (accessed August 2021).
- PyTorch. 2021c. “LSTM”. Available at: https://pytorch.org/docs/stable/generated/-torch.nn.LSTM.html. (accessed October 2021).
- 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
- 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.
- 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)
- 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
- 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.
- Smieja, M.Ł. Struski, J. Tabor, B. Zieliński, and P. Spurek. 2019. Processing of Missing Data by Neural Networks.
- 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).
- 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
- 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.
- Stratos, K. 2020. Feedforward and Recurrent Neural Networks. Available at: http://www1.cs.columbia.edu/,stratos/research/neural.pdf. (accessed December 2020)
- “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.
- 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).
- UNCTAD. 2020a. “UNCTADStat”. UNCTADStat. 2020. Available at: https://unctadstat.unctad.org/EN/Index.html. (accessed March 2021).
- 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).
- 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).
- “The X-13ARIMA-SEATS Seasonal Adjustment Program”. The X-13ARIMA-SEATS Seasonal Adjustment Program. 2017. Available at:https://www.census.gov/srd/www/-x13as/.
- 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).
- WMO. 2017. “Guidelines for Nowcasting Techniques”. 1198. WMO. WMO. Available at: https://library.wmo.int/doc_num.php?explnum_id=3795. (accessed March 2021).
- 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).
- 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.