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