Politická ekonomie 2019, 67(4):347-370 | DOI: 10.18267/j.polek.1243

Možnosti odhadů krátkodobých makroekonomických agregátů na základě výsledků konjunkturních průzkumů

Luboš Mareka, Stanislava Hronováa, Richard Hindlsa
a Vysoká škola ekonomická v Praze, Fakulta informatiky a statistiky

Possibilities of Estimations of Short-term Macroeconomic Aggregates Based on Business Survey Results

The aim of the article is to construct a model for estimating the quarterly gross value added (GVA) of the national economy (GDP) based on the results of business surveys (so-called confidence indicators) in industry, construction, commerce and services (incl. banking sector), and to set the forecast for four quarters ahead. The suitability of the applied approach is assessed using pairwise dependencies for individual sectors. In the case of both pairwise and multidimensional dependencies, the authors proceed from a linear dynamic model, which is a combination of ARIMA models (or SARIMA models) in conjunction with regression analysis, where the variables explained are time-shifted. The quality of the estimated models is proven to be very high. The analysis shows a significant link between the sector's gross value added and sectoral confidence indicators. Significant predictors of the GVA of the national economy and GDP show explanatory variables of confidence indicators in industry and construction, whereas indicators of confidence in trade and services were statistically insignificant. Timely knowledge of these indicators in conjunction with linear dynamic models allows better and faster predictions of quarterly GVA and GDP than with conventional time series models.

Keywords: business surveys, forecasting, business expectations, short-term GDP forecasting, time series analsis
JEL classification: C22, E01, E32

Received: April 8, 2018; Accepted: January 30, 2019; Published: September 3, 2019  Show citation

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Marek, L., Hronová, S., & Hindls, R. (2019). Possibilities of Estimations of Short-term Macroeconomic Aggregates Based on Business Survey Results. Politická ekonomie, 67(4), 347-370. doi: 10.18267/j.polek.1243
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    References

    1. Abberger, K. (2005). The Use of Qualitative Business Tendency Surveys for Forecasting Business Investment in Germany. Ifo. Working Paper No. 13.
    2. Bessec, M. (2010). Etalonnage du taux de croissance du PIB francais sur la base des enquêtes de conjoncture. Économie et Prévision, 193(2), 77-99, https://doi.org/10.3406/ecop.2010.8036 Go to original source...
    3. Biau, O., Erkel-Rousse, H., Ferrari, N. (2006). Réponses individuelles aux enquêtes de conjoncture et prévision macroéconomique: Exemple de la prévision de la production manufacturière. Économie et Statistique, 395-396(1), 91-116, https://doi.org/10.3406/estat.2006.7133 Go to original source...
    4. Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (1994) Time Series Analysis; Forecasting and Control. 3rd Edition, New Jersey: Prentice Hall, Englewood Cliff. ISBN 9780130607744.
    5. Constantini, M. (2013). Forecasting the Industrial Production Using Alternative Factor Models and Business Survey Data. Journal of Applied Statistics, 40(10), 2275-2289, https://doi.org/10.1080/02664763.2013.809870 Go to original source...
    6. Driver, C., Urga, G. (2004). Transforming Qualitative Survey Data: Performance Comparisons for the UK. Oxford Bulletin of Economics and Statistics, 66(1), 71-89, https://doi.org/10.1111/j.1468-0084.2004.00070.x Go to original source...
    7. European Union (2016). The Joint Harmonised EU Programme of Business and Consumer Surveys. EU, Brusel.
    8. Dubois, E., Michaux, E. (2006). Étalonnages à l'aide d'enquêtes de conjoncture : de nouveaux résultats. Économie et Prévision, 172(1), 11-28, https://doi.org/10.3406/ecop.2006.7477 Go to original source...
    9. Fanouillet, J.-C., Salanié, B. (1990). Prévoir la consommation et la production grâce aux enquêtes de la conjoncture. Economie et Statistique, 234(1), 25-32, https://doi.org/10.3406/estat.1990.5473 Go to original source...
    10. Fayolle, J. (1987). Pratique contemporaine de l´analyse conjoncturelle. Paris: Economica. ISBN 978-2717813685.
    11. Hansson, J., Jansson, P., Lof, M. (2005). Business Survey Data: Do They Help in Forecasting GDP Growth? International Journal of Forecasting, 21(2), 377-389, https://doi.org/10.1016/j.ijforecast.2004.11.003 Go to original source...
    12. INSEE (2007). Enquête de conjoncture sur la situation et les perspectives dans l´industrie - méthodologie. INSEE - Méthodes No.o117.
    13. Jobert, T., Persyn, L. (2012). Que nous apprennent les enquêtes de conjoncture dansl'industrie? Revue d'économie industrielle, 138, 39-64, https://doi.org/10.4000/rei.5365 Go to original source...
    14. Klein, L. R., Ozmucur, S. (2010). The Use of Consumer and Business Surveys in Forecasting. Economic Modelling, 27(6), 1453-1462, https://doi.org/10.1016/j.econmod.2010.07.005 Go to original source...
    15. Lui, S., Mitchell, J., Weale, M. (2011). Qualitative Business Surveys: Signal or Noise? Journal of the Royal Statistical Society, 174(2), 327-348, https://doi.org/10.1111/j.1467-985x.2010.00667.x Go to original source...
    16. Martinsen, K., Ravazzolo, F., Wulfsgerg, F. (2014). Forecasting Macroeconomic Variables Using Disaggregate Survey Data. International Journal of Forectasting, 30(1), 65-77, https://doi.org/10.1016/j.ijforecast.2013.02.003 Go to original source...
    17. Mitchell, J., Smith, R. J., Weale, M. R. (2006). A Bayesian Indicator of Manufacturing Output from Qualitative Business Panel Survey Data. National Institute of Economic and Social research: Cambridge. Discussion Paper No. 261.
    18. OECD (2003). Manuel des enquêtes de conjoncture auprès des entreprises. Paříž: OECD.
    19. Pankratz, A. (1991). Forecasting with Dynamic Regression Models. New York: John Wiley & Sons. ISBN 978-0-471-61528-6. Go to original source...
    20. Pošta, V., Pikhart, Z. (2012). The Use of the Sentiment Economic Indicator for GDP Short-term Forecasting: Evidence from EU Economies. Statistika, 49(1), 41-55.
    21. Reynaud, M., Scherrer, S. (1996). Une modelisation VAR de l´enquete mensuelle de conjoncture de l´INSEE dans l´industrie. Ministere de l´économie, des finances et de l´industrie. Document de travail, No. 96-12.
    22. SCA Statistical System (1991). Reference Manual for General Statistical Analysis. USA: Scientific Associates Corp. Oak Brook.
    23. Stock, J. H., Watson, M. W. (2002). Macroeconomic Forcasting Using Diffusion Indexes. Journal of Business and Economic Statistics, 20(2), 147-162, https://doi.org/10.1198/073500102317351921 Go to original source...

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