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Machine Learning in Macro-Economic Series Forecasting

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  • 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. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    2. 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.
    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. 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.
    5. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    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. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
    3. 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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    JEL classification:

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

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