Can Machine Learning Models Predict Inflation?
References
- Ang, A., Bekaert, G., & Wei, M. (2007). Do macro variables, asset markets, or surveys forecast inflation better?. <em>Journal of monetary Economics</em>, <em>54</em>(4), 1163-1212.
- Atkeson, Andrew and Lee E. Ohanian (2001): Are Phillips Curves Useful for Forecasting Inflation?, Federal Reserve Bank of Minneapolis Quarterly Review, 25, pp.2-11
- Bai, Jushan and Serena Ng (2008): Forecasting Economic Time Series Using Targeted Predictors, Journal of Econometrics, 146, pp.304-317.
- Batini, N., & Haldane, A. (1999). Forward-looking rules for monetary policy. In <em>Monetary policy rules</em> (pp. 157-202). University of Chicago Press.
- Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. <em>IEEE transactions on neural networks</em>, <em>5</em>(2), 157-166.
- Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In <em>Proceedings of the fifth annual workshop on Computational learning theory</em> (pp. 144-152).
- Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth Int. Group, 37(15), 237-251.
- Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
- Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
- Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In <em>Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining</em> (pp. 785-794).
- Dotsey, Michael, Shigeru Fujita and Tom Stark (2011): Do Phillips Curves Conditionally Help to Forecast Ination, working paper
- Faust, J., and Wright, J. (2013), “Forecasting Inflation,” in Handbook of Economic Forecasting (Vol. 2A), eds. G. Elliott and A. Timmermann, Amsterdam: Elsevier.
- Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. <em>Neural networks</em>, <em>18</em>(5-6), 602-610.
- Guyon, I., Boser, B., & Vapnik, V. (1993). Automatic capacity tuning of very large VC-dimension classifiers. In <em>Advances in neural information processing systems</em> (pp. 147-155).
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. <em>Neural computation</em>, <em>9</em>(8), 1735-1780.
- Inoue, A., and Kilian, L. (2008), “How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. CPI Inflation,” <em>Journal of the American Statistical Association</em>, 103, 511–522.
- Ivașcu, C. F. (2021). Option pricing using machine learning. <em>Expert Systems with Applications</em>, <em>163</em>, 113799.
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In <em>Advances in neural information processing systems</em> (pp. 3146-3154).
- Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. <em>PloS one</em>, <em>13</em>(3), e0194889.
- Mandalinci, Z. (2017). Forecasting inflation in emerging markets: An evaluation of alternative models. <em>International Journal of Forecasting</em>, <em>33</em>(4), 1082-1104.
- Manzan, S., & Zerom, D. (2013). Are macroeconomic variables useful for forecasting the distribution of US inflation?. <em>International Journal of Forecasting</em>, <em>29</em>(3), 469-478.
- Medeiros, M., and Mendes, E. (2016), “1-Regularization of High-Dimensional Time-Series ModelsWith Non-Gaussian and Heteroskedastic Errors,” <em>Journal of Econometrics</em>, 191, 255–271.
- Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., & Zilberman, E. (2021). Forecasting inflation in a data-rich environment: the benefits of machine learning methods. <em>Journal of Business & Economic Statistics</em>, <em>39</em>(1), 98-119.
- Nakamura, E. (2005), “Inflation Forecasting Using a Neural Network,” <em>Economics Letters</em>, 86, 373–378.
- Özgür, Ö., & Akkoç, U. (2021). Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms. <em>International Journal of Emerging Markets</em>.
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. <em>nature</em>, <em>323</em>(6088), 533-536.
- Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. <em>Statistics and computing</em>, <em>14</em>(3), 199-222.
- Stock, J. H., & Watson, M. W. (1999). Forecasting inflation. <em>Journal of Monetary Economics</em>, <em>44</em>(2), 293-335.
- Stock, J., and Watson, M. (2010), “Modeling Inflation after the Crisis,” Technical Report, National Bureau of Economic Research.
- Ülke, V., Sahin, A., & Subasi, A. (2018). A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA. <em>Neural Computing and Applications</em>, <em>30</em>(5), 1519-1527.
- Vapnik, V. (1995). The nature of statistical learning theory. Berlin: Springer.
- Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In <em>Advances in neural information processing systems</em> (pp. 281-287).
- Zahara, S., & Ilmiddaviq, M. B. (2020). Consumer price index prediction using Long Short Term Memory (LSTM) based cloud computing. In <em>Journal of Physics: Conference Series</em> (Vol. 1456, No. 1, p. 012022). IOP Publishing.
DOI: https://doi.org/10.2478/picbe-2023-0155 | Journal eISSN: 2558-9652
Language: English
Page range: 1748 - 1756
Published on: Jul 14, 2023
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Related subjects:
© 2023 Codruț Ivașcu, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.