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Exploring the dynamics of business survey data using Markov models

Author

Listed:
  • W. Hölzl

    (Austrian Institute of Economic Research (WIFO))

  • S. Kaniovski

    (Austrian Institute of Economic Research (WIFO))

  • Y. Kaniovski

    (Free University of Bozen-Bolzano)

Abstract

Business tendency surveys are widely used for monitoring economic activity. They provide timely feedback on the current business conditions and outlook. We identify the unobserved macroeconomic factors behind the distribution of quarterly responses by Austrian firms on the questions concerning the current business climate and production. The aggregate models identify two macroeconomic regimes: upturn and downturn. Their dynamics is modeled using a regime-switching matrix. The micro-founded models envision dependent responses by the firms, so that a favorable or an adverse unobserved common macroeconomic factor increases the frequency of optimistic or pessimistic responses. The corresponding conditional transition probabilities are estimated using a coupling scheme. Extensions address the sector dimension and introduce dynamic common tendencies modeled with a hidden Markov chain.

Suggested Citation

  • W. Hölzl & S. Kaniovski & Y. Kaniovski, 2019. "Exploring the dynamics of business survey data using Markov models," Computational Management Science, Springer, vol. 16(4), pages 621-649, October.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:4:d:10.1007_s10287-019-00354-4
    DOI: 10.1007/s10287-019-00354-4
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    References listed on IDEAS

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    More about this item

    Keywords

    Business tendency surveys; Business cycle; Coupled Markov chain; Multinomial distribution;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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