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Quantifying the qualitative responses of the output purchasing managers index in the US and the Euro area

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  • Vermeulen, Philip

Abstract

The survey based monthly US ISM production index and Eurozone manufacturing PMI output index provide early information on industrial output growth before the release of the official industrial production index. I use the Carlson and Parkin probability method to construct monthly growth estimates from the qualitative responses of the US ISM production index and the Eurozone manufacturing PMI output index. I apply the method under different assumptions on the cross-sectional distribution of output growth using the uniform, logistic and Laplace distribution. I show that alternative distribution assumptions lead to very similar estimates. I also test the performance of the different growth estimates in an out of sample forecasting exercise of actual industrial production growth. All growth estimates beat a simple autoregressive model of output growth. Distribution assumptions again matter little most of the time except during the financial crisis when the estimates constructed using the Laplace distributional assumption perform the best. My findings are consistent with recent findings of Bottazzi and Sechi (2006) that the distribution of firm growth rates has a Laplace distribution. JEL Classification: C18, E27

Suggested Citation

  • Vermeulen, Philip, 2012. "Quantifying the qualitative responses of the output purchasing managers index in the US and the Euro area," Working Paper Series 1417, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20121417
    Note: 327651
    as

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    References listed on IDEAS

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    1. Dasgupta, Susmita & Lahiri, Kajal, 1992. "A Comparative Study of Alternative Methods of Quantifying Qualitative Survey Responses Using NAPM Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 391-400, October.
    2. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    3. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    4. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    5. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
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    Cited by:

    1. Fumio Hayashi & Yuta Tachi, 2023. "Nowcasting Japan’s GDP," Empirical Economics, Springer, vol. 64(4), pages 1699-1735, April.
    2. Kilinc, Zubeyir & Yucel, Eray, 2016. "PMI Thresholds for GDP Growth," MPRA Paper 70929, University Library of Munich, Germany.
    3. Schnatz, Bernd & D'Agostino, Antonello, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.
    4. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    5. Gabe J. Bondt & Stefano Schiaffi, 2015. "Confidence Matters for Current Economic Growth: Empirical Evidence for the Euro Area and the United States," Social Science Quarterly, Southwestern Social Science Association, vol. 96(4), pages 1027-1040, December.

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    More about this item

    Keywords

    Carlson-Parkin method; diffusion index; forecasting; ISM; PMI; purchasing managers’ surveys; qualitative response data;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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