# Exploring the dynamics of business survey data using Markov models

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## 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.

## Keywords

Business tendency surveys Business cycle Coupled Markov chain Multinomial distribution## Mathematics Subject Classification

90C30 90C90## JEL Classification

C13 D84 E37## Notes

## References

- Alfó M, Bartolucci F (2015) Latent variable models for the analysis of socio-economic data. Metron 7(2):151–154CrossRefGoogle Scholar
- Anderson O (1951) Konjunkturtest und Statistik. Möglichkeiten und Grenzen einer Quantifizierung von Testergebnissen. Allg Stat Arch 35:209–220Google Scholar
- Bachmann R, Elstner S (2015) Firm optimism and pessimism. Eur Econ Rev 79:297–325CrossRefGoogle Scholar
- Boreiko DV, Kaniovski S, Kaniovski YM, Ch Pflug G (2017) Identification of hidden Markov chains governing dependent credit-rating migrations. Commun Stat Theory Methods 48:75–87 CrossRefGoogle Scholar
- Boreiko DV, Kaniovski YM, Pflug GCh (2016) Modeling dependent credit rating transitions—a comparison of coupling schemes and empirical evidence. Cent Eur J Oper Res 24(4):989–1007CrossRefGoogle Scholar
- Caballero RJ, Engel E (2003) Adjustment is much slower than you think, Working Paper, MITGoogle Scholar
- Cesaroni T (2011) The cyclical behavior of the Italian business survey data. Empir Econ 41:747–768CrossRefGoogle Scholar
- Cox BG, Binder DA, Chinnappa BN, Christianson A, Colledge MJ, Kott PS (2011) Business survey methods. Wiley, New YorkGoogle Scholar
- European Commission (2014) A user manual to the joint harmonised EU programme of business and consumers surveys, Brussels, 2014Google Scholar
- Filardo AJ (1994) Business-cycle phases and their transitional dynamics. J Bus Econ Stat 12:299–308Google Scholar
- Filardo AJ, Gordon SF (1998) Business cycle durations. J Econom 85:99–123CrossRefGoogle Scholar
- Frühwirth-Schnatter S (2006) Finite mixture and Markov switching models. Springer series in statistics. Springer, BerlinGoogle Scholar
- Geil P, Zimmermann K (1996) Quantifizierung qualitativer Daten. In: Oppenländer KH (ed) Konjunkturindikatoren: Fakten, Analysen, Verwendung. Oldenbourg, München, pp 108–130Google Scholar
- Goldrian G (2007) Handbook of survey-based business cycle analysis. Edward Elgar Publishing, CheltenhamCrossRefGoogle Scholar
- Hölzl W, Kaniovski S, Reinstaller A (2015) The exposure of technology and knowledge intense sectors to the business cycle. Bull Appl Econ 2(1):1–19Google Scholar
- Hölzl W, Schwarz G (2014) Der WIFO-Konjunkturtest: Methodik und Prognoseeigenschaften. WIFO Monatsberichte 87(12):835–850Google Scholar
- Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384CrossRefGoogle Scholar
- Kaniovski YM, Pflug GCh (2007) Risk assessment for credit portfolios: a coupled Markov chain model. J Bank Finance 31(8):2303–2323CrossRefGoogle Scholar
- Kaufmann D, Scheufele R (2017) Business tendency survey and macroeconomic fluctuations. Int J Forecast 33(4):878–893CrossRefGoogle Scholar
- Knetsch Th (2005) Evaluating the German inventory cycle using data from the Ifo business survey. In: Strum J-E (ed) Ifo survey data in business cycle and monetary policy analysis. Springer, Berlin, pp 61–92CrossRefGoogle Scholar
- Müller C, Köberl E (2007) The speed of adjustment to demand shocks: a Markov-chain measurement using micro panel data, KOF Swiss Economic Institute at the Swiss Federal Institute of Technology Zurich, Working Paper, No. 170Google Scholar
- OECD (2003) Business tendency surveys: a handbook. OECD, ParisCrossRefGoogle Scholar
- Skrondal A, Rabe-Hesketh S (2007) Latent variable modelling: a survey. Scand. J. Stat. 34(4):712–745CrossRefGoogle Scholar
- Stock JH, Watson MW (2011) Dynamic factor models. In: Clements MP, Hendry DF (eds) The Oxford handbook of economic forecasting. Oxford University Press, OxfordGoogle Scholar
- Wozabal D, Hochreiter R (2012) A coupled Markov chain approach to credit risk modeling. J Econ Dyn Control 36(3):403–415CrossRefGoogle Scholar