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Real-time Forecasting of Colombian Consumer Price Inflation Using High-Dimensional Methods
Pronósticos en tiempo real de la inflación de los precios al consumidor en Colombia con métodos de alta dimensión
DOI:
https://doi.org/10.15446/rce.v48n2.116414Keywords:
Ensembles, Forecasting inflation, Lasso, Machine learning, Penalized regression, Shrinkage. (en)Aprendizaje de máquinas, Lasso, Métodos de ensamble, Pronósticos de la inflación, Regresión penalizada. (es)
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In this paper, we examine the effectiveness of high-dimensional methods for real-time forecasting of inflation in Colombia. We utilize statistical dimension reduction techniques, such as sparse principal components and dynamic factor analysis, alongside machine learning algorithms that incorporate shrinkage methods. Our evaluation of out-of-sample forecasts, using a dataset of 102 macroeconomic and financial indicators, indicates that ensembles of multiple underlying models can enhance forecast accuracy for horizons of 11 and 12 months ahead. Additionally, stepwise models are suitable for horizons between 4 and 10 months ahead, and spectral component models are effective for short horizons.
En este documento, se examina la efectividad de los métodos de alta dimensión para pronosticar en tiempo real la inflación en Colombia. Se utilizan técnicas estadísticas de reducción de dimensión, como componentes principales dispersos y análisis factorial dinámico, junto con algoritmos de aprendizaje automático. La evaluación de pronósticos fuera de muestra, utilizando 102 indicadores macroeconómicos y financieros, indica que la síntesis de múltiples modelos subyacentes mejora la precisión de las predicciones para horizontes de 11 y 12 meses. Además, los modelos secuenciales son adecuados para horizontes de entre 4 y 10 meses, y los modelos de componentes espectrales son efectivos para horizontes cortos.
References
Araujo, G. & Gaglianone, W. (2020), 'Machine learning methods for inflation forecasting in brazil: new contenders versus classical models', Central Bank of Barzil Working Paper pp. 1-39.
Both, T. & Nibbering, D. (2020), Subspace methods, Springer, chapter 9, pp. 267-326. In: Macroeconomic Forecasting in the Era of Big Data: Theory and Practice. DOI: https://doi.org/10.1007/978-3-030-31150-6_9
Chernozhukov, V., Hansen, C. & Liao, Y. (2017), 'A lava attack on the recovery of sums of dense and sparse signals', Annals of Statistics 45, 37-76. DOI: https://doi.org/10.1214/16-AOS1434
Elliot, G., Gargano, A. & Timmermann, A. (2015), 'Complete subset regression with large dimensional sets of predictors', Journal of Economics Dynamics and Control 54(54), 86-110. DOI: https://doi.org/10.1016/j.jedc.2015.03.004
Fulton, C. & Hubrisch, K. (2020), 'Forecasting us inflation in real time', Finance and Economics Discussion Series 2021-014. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2021.014. pp. 1-30. DOI: https://doi.org/10.17016/feds.2021.014
Garcia, M., Medeiros, M. & Vasconcelos, G. (2017), 'Real-time inflation forecasting with high-dimensional models: The case of brazil', International Journal of Forecasting 33(3), 679-693. DOI: https://doi.org/10.1016/j.ijforecast.2017.02.002
Gianone, D., Lenza, W. & Primiceri, G. (2018), 'Economic predictions with big data: The illusion of sparsity', Federal Reserve Bank of New York Sta Reports, no. 847 pp. 1-26. DOI: https://doi.org/10.2139/ssrn.3166281
Hastie, T., Tishbirani, R. & Friedman, J. (2001), The elemnts of statistical learning: data mining, inference, and prediction, 2 edn, Springer.
Inoue, A. & Killian, L. (2008), 'How useful is bagging in forecasting economic time series? a case study of u.s. consumer price inflation', Journal of the American Statistical Association 103(482), 511-522. DOI: https://doi.org/10.1198/016214507000000473
Kim, H. & Swanson, N. (2014), 'Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence', Journal of econometrics (178), 352-367. DOI: https://doi.org/10.1016/j.jeconom.2013.08.033
Kim, H. & Swanson, N. (2018), 'Mining big data using parsimonous factor, machine learning, variable selection and shrinkage methods', International Journal of Forecasting 34(2), 339-354. DOI: https://doi.org/10.1016/j.ijforecast.2016.02.012
Kock, A., M. M. & Vasconcelos, G. (2020), Penalized Time Series Regression, Springer, chapter 7, pp. 193_277. In: Arango, L. and Hamann, F. ed., (2020). Macroeconomic Forecasting in the Era of Big Data: Theory and Practice. DOI: https://doi.org/10.1007/978-3-030-31150-6_7
Marcellino, M., Stock, J. & Watson, M. (2006), 'A comparison of direct and iterated multistep ar methods for forecasting macroeconomic time series', Journal of econometrics 135(135), 499-526. DOI: https://doi.org/10.1016/j.jeconom.2005.07.020
Peña, D., Tsay, R. & Zamar, R. (2019), 'Empirical dynamic quantiles for visualizating of high-dimensional time series', Technometrics 61, 429-444. DOI: https://doi.org/10.1080/00401706.2019.1575285
Silva, G. & Gaglianone, W. (2020), 'Machine learning methods for inflation forecasting in brazil. new contenders versus classical models', Journal of Business and Economic Statistics 30(30), 481-493.
Stock, J. & Watson, M. (2012), 'Generalized shrinkage methods for forecasting using many predictors', Journal of Business and Economic Statistics 30(30), 481-493. DOI: https://doi.org/10.1080/07350015.2012.715956
Wainwright, M. (2019a), Introduction, Cambridge Series in Statistical and Probabilistic Mathematics, chapter 1, pp. 236_257. In: High-Dimensional Statistics: A Non-symptotic Viewpoint. DOI: https://doi.org/10.1017/9781108627771
Wainwright, M. (2019b), Principal component anlysis in high-dimensions, Cambridge Series in Statistical and Probabilistic Mathematics, chapter 8, pp. 236-257. In: High-Dimensional Statistics: A Non-Asymptotic Viewpoint. DOI: https://doi.org/10.1017/9781108627771.008
Zou, H., Hastie, T. & Tibshirani, R. (2006), 'Sparse principal component analysis', Journal of Computational and Graphical Statistics 15(2), 265-286. DOI: https://doi.org/10.1198/106186006X113430
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