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Machine Learning in Macro-Economic Series Forecasting

Author

Listed:
  • Yun Liao

Abstract

In this paper I conducted a simple experiment to using Artificial Neural Network in time-series forecasting, by combining First order Markov Switching Model and K-means algorithms, the forecasting performance of machine learning has outperformed the benchmark of time-series inflation rate forecasting. The paper reveal the potential of ANN forecasting, also provide future direction of research.

Suggested Citation

  • Yun Liao, 2017. "Machine Learning in Macro-Economic Series Forecasting," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(12), pages 71-76, December.
    • Handle: RePEc:ibn:ijefaa:v:9:y:2017:i:12:p:71-76
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    References listed on IDEAS

    as
    1. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    2. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    5. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    6. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
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    Cited by:

    1. Anastasios Petropoulos & Vassilis Siakoulis & Konstantinos P. Panousis & Loukas Papadoulas & Sotirios Chatzis, 2023. "Macroeconomic forecasting and sovereign risk assessment using deep learning techniques," Papers 2301.09856, arXiv.org.
    2. Massimo Guidolin, Mingwei (Max) Liang, Milena Petrova, 2026. "The Importance of Considering Regimes in Long-term Asset Allocation to Real Estate," BAFFI CAREFIN Working Papers 26263, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    3. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
    4. Jesús Alejandro Navarro Acosta & Valeria Soto Mendoza & Laura Policardo & Edgar Javier Sánchez Carrera, 2025. "Modeling economic growth in pandemic times with machine learning regression algorithms," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 20(1), pages 1-33, Enero - M.

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    Keywords

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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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