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Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland

Author

Listed:
  • Alain Galli

    (Swiss National Bank)

  • Christian Hepenstrick

    (Swiss National Bank)

  • Rolf Scheufele

    (Swiss National Bank)

Abstract
We compare several methods for monitoring short-term economic developments in Switzerland. Based on a large mixed-frequency data set, the following approaches are presented and discussed: a factor-based information combination approach (a dynamic factor model based on the Kalman filter/smoother and estimated by the EM algorithm), a model combination approach resting on MIDAS regression models, and a variable selection approach using a specific-to-general algorithm. In an out-of-sample GDP forecasting exercise, we show that the considered approaches clearly beat relevant benchmarks such as univariate time-series models and models that work with just one indicator. Moreover, we find that the factor model is superior to the other two approaches under investigation. However, forecast pooling of the three methods turns out to be even more promising.

Suggested Citation

  • Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    • Handle: RePEc:ijc:ijcjou:y:2019:q:2:a:5
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    Cited by:

    1. Erhan Uluceviz & Kamil Yilmaz, 2020. "Real-financial connectedness in the Swiss economy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-20, December.
    2. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    3. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    4. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
    5. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    6. Liudmila Kitrar & Tamara Lipkind, 2021. "Assessment Of GDP Growth After The Corona Crisis Using The Results Of Business And Consumer Surveys," HSE Working papers WP BRP 118/STI/2021, National Research University Higher School of Economics.

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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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