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Business confidence and cyclical turning points: a Markov-switching approach

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  • Mark J. Holmes
  • Brian Silverstone

Abstract

Markov regime-switching analysis is used to consider the relationship between business confidence and the probability of turning points in cyclical GDP. We find, in an application to New Zealand, that confidence is related to both the deepness and duration of the business cycle and is asymmetric regarding the probability of the economy remaining in a given regime. Overall, the New Zealand business confidence series is a useful indicator of cyclical turning points.

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  • Mark J. Holmes & Brian Silverstone, 2010. "Business confidence and cyclical turning points: a Markov-switching approach," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 229-233, February.
    • Handle: RePEc:taf:apeclt:v:17:y:2010:i:3:p:229-233
    DOI: 10.1080/13504850701720247
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    References listed on IDEAS

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    1. 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.
    2. Roy Batchelor, 2001. "Confidence indexes and the probability of recession: a Markov switching model," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 36(1), pages 107-124, January.
    3. Karl Taylor & Robert McNabb, 2007. "Business Cycles and the Role of Confidence: Evidence for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(2), pages 185-208, April.
    4. Mr. Abdul d Abiad, 2003. "Early Warning Systems: A Survey and a Regime-Switching Approach," IMF Working Papers 2003/032, International Monetary Fund.
    5. Massmann, Michael & Mitchell, James & Weale, Martin, 2003. "Business Cycles and Turning Points: A Survey of Statistical Techniques," National Institute Economic Review, National Institute of Economic and Social Research, vol. 183, pages 90-106, January.
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    Cited by:

    1. Hamid Baghestani & Ajalavat Viriyavipart, 2019. "Do factors influencing consumer home-buying attitudes explain output growth?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(5), pages 1104-1115, August.
    2. Helder Ferreira de Mendonça & Eduardo Schirmer Finn, 2022. "Can credibility offset electricity price effect on business confidence? An empirical investigation from a large emerging economy," Applied Economics, Taylor & Francis Journals, vol. 54(11), pages 1229-1242, March.
    3. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    4. Naomi Moy & Ho Fai Chan & Frank Mathmann & Markus Schaffner & Benno Torgler, 2021. "Confidence is good; too much, not so much: Exploring the effects on reward-based crowdfunding success," CREMA Working Paper Series 2021-18, Center for Research in Economics, Management and the Arts (CREMA).

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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