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Empirical Economics

, Volume 41, Issue 2, pp 293–309 | Cite as

The inflation and inflation uncertainty relationship for Turkey: a dynamic framework

  • M. Hakan BerumentEmail author
  • Yeliz Yalcin
  • Julide O. Yildirim
Article

Abstract

This article assesses the interaction between inflation and inflation uncertainty in a dynamic framework for Turkey by using monthly data for the time period 1984–2009. The bulk of previous studies investigating the link between inflation and inflation uncertainty employ Autoregressive Conditional Heteroskedasticity (ARCH)-type models, which consider inflation uncertainty as a predetermined function of innovations to inflation specification. The stochastic volatility in mean (SVM) models that we use allow for gathering innovations to inflation uncertainty and assess the effect of inflation volatility shocks on inflation over time. When we assess the interaction between inflation and its volatility, the empirical findings indicate that response of inflation to inflation volatility is positive and statistically significant. However, the response of inflation volatility to inflation is negative but not statistically significant.

Keywords

Inflation Inflation uncertainty Stochastic volatility models VAR Impulse response 

JEL Classification

C15 C22 E31 

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References

  1. Andersen T, Chung H, Sorensen BE (1999) Efficient method of moments estimation of a stochastic volatility model: a Monte Carlo study. J Econ 91: 61–87Google Scholar
  2. Asaf A (2006) The stochastic volatility in mean model and automation: evidence from TSE. The Quart Rev Econ Financ 46: 241–253CrossRefGoogle Scholar
  3. Ball L (1992) Why does high inflation raise inflation uncertainty?. J Monet Econ 29: 371–388CrossRefGoogle Scholar
  4. Berument H, Dincer NN (2005) Inflation and inflation uncertainty in the G-7 countries. Physica A 348: 371–379CrossRefGoogle Scholar
  5. Berument H, Metin-Ozcan K, Neyapti B (2001) Modelling inflation uncertainty using EGARCH: an application to Turkey. http://www.econturk.org/Turkisheconomy/kivil2.pdf
  6. Berument H, Kilinc Z, Ozlale U (2005) The missing link between inflation uncertainty and interest rates. Scottish J Political Econ 52: 2–241Google Scholar
  7. Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31: 307–327Google Scholar
  8. Broto C, Ruiz E (2004) Estimation methods for stochastic volatility models: a survey. J Econ Surveys 18: 5CrossRefGoogle Scholar
  9. Brunner AD, Hess G (1993) Are higher levels of inflation less predictable? A state-dependent conditional heteroskedasticity approach. J Bus Econ Stat 11: 187–197CrossRefGoogle Scholar
  10. Chen CWS, Gerlach R, So MKP (2008) Bayesian model selection for heteroskedastic models. In: Chib S, Griffiths B, Koop G, Terrell D (eds) Bayesian econometric methods. Advances in econometrics. Elsevier Science, Amsterdam, pp 567–594CrossRefGoogle Scholar
  11. Cukierman A (1992) Central bank strategy, credibility and independence: theory and evidence. MIT Press, CambridgeGoogle Scholar
  12. Cukierman A, Meltzer A (1986) A theory of ambiguity credibility and inflation under discretion and asymmetric information. Econometrica 54: 1099–1128CrossRefGoogle Scholar
  13. Danielsson J (1994) Stochastic volatility in asset prices, estimation with simulated maximum likelihood. J Econ 64: 375–400Google Scholar
  14. De Jong P, Shepard N (1995) The simulation smoother for time series models. Biometrika 82: 339–350CrossRefGoogle Scholar
  15. Doornik J (1998) Object-oriented matrix programming using Ox 2.0. London: Timberlake Consultants Ltd. http://www.nuff.ox.ac.uk/Users/Doornik
  16. Durbin J, Koopman SJ (1997) Monte carlo maximum likelihood for non-gaussian state space models. Biometrika 84: 669–684CrossRefGoogle Scholar
  17. Durbin J, Koopman SJ (2002) A simple and efficient simulation smoother for state space time series analysis. Biometrika 3: 603–616CrossRefGoogle Scholar
  18. Elder J (2004) Another perspective on the effects of inflation uncertainty. J Money Credit Banking 36(5): 911–928CrossRefGoogle Scholar
  19. Engle R (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation. Econometrica 50: 987–1008CrossRefGoogle Scholar
  20. Ergun M (2000) Electoral political business cycles in emerging markets: evidence from Turkey Russian and East European. Financ Trade 36(6): 6–32Google Scholar
  21. Evans M (1991) Discovering the link between inflation rates and inflation uncertainty. J Money Credit Banking 23: 169–184CrossRefGoogle Scholar
  22. Franses PhH, van der Leij MJ, Paap R (2008) A simple test for GARCH against a stochastic volatility model. J Financ Econ 6(3): 291–306Google Scholar
  23. Friedman M (1977) Nobel lecture: inflation and unemployment. J Political Econ 85: 451–472CrossRefGoogle Scholar
  24. Gallant AR, Hsieh DA, Tauchen GE (1997) Estimation of stochastic volatility models with diagnostics. J Econ 81: 159–192Google Scholar
  25. Grier K, Perry MJ (1998) On inflation and inflation uncertainty in the G7 countries. J Int Money Financ 17: 671–689CrossRefGoogle Scholar
  26. Grier K, Perry MJ (2000) The effects of real and nominal uncertainty on inflation and output growth: some GARCH-M evidence. J Appl Econ 15(1): 445–458CrossRefGoogle Scholar
  27. Harvey AC, Ruiz E, Shephard N (1994) Multivariate stochastic variance models. Rev Econ Stud 61: 247–264CrossRefGoogle Scholar
  28. Hamilton JD (1994) Time series analysis. Princeton University Press, PrincetonGoogle Scholar
  29. Hol E, Koopman SJ (2000) Forecasting the variability of stock index returns with stochastic volatility models and implied volatility. http://www.timbergen.nl
  30. Holland S (1993) Comment on inflation regimes and the sources of inflation uncertainty. J Money Credit Banking 25: 514–520Google Scholar
  31. Holland S (1995) Inflation and uncertainty: tests for temporal ordering. J Money Credit Banking 27: 827–837CrossRefGoogle Scholar
  32. Jacquier E, Polson NG, Rossi PE (1994) Bayesian analysis of stochastic volatility models (with discussion). J Bus Econ Stat 12: 371–389CrossRefGoogle Scholar
  33. Kim S, Shephard N, Chib S (1998) Stochastic volatility: likelihood inference and comparison with ARCH models. Rev Econ Stud 65: 361–394CrossRefGoogle Scholar
  34. Klein B (1978) The measurement on long and short-term price uncertainty: a moving regression time series analysis. Econ Inq 16: 438–452CrossRefGoogle Scholar
  35. Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econ 74(1): 119–147Google Scholar
  36. Koopman SJ, Uspensky EH (2002) The stochastic volatility in mean model: empirical evidence from international stock markets. J Appl Econ 17: 667–689CrossRefGoogle Scholar
  37. Koopman SJ, Shephard N, Doornik JA (1999) Statistical algorithms for models in state space form using SsfPack 22. Econ J 2: 113–166Google Scholar
  38. Krichene N (2003) Modeling stochastic volatility with application to stock returns IMF Working Papers, No 03/125. http://www.imf.org/e.../wp/2003/wp03125.pdf
  39. Melino A, Turnbull SM (1990) Pricing foreign currency options with stochastic volatility. J Econ 45: 239–265Google Scholar
  40. Nas TF, Perry MJ (2000) Inflation, inflation uncertainty, and monetary policy in Turkey: 1960–1998. Contemp Econ Policy 18(2): 170–180Google Scholar
  41. Neyapti B (2000) Inflation and inflation uncertainty in Turkey: evidence from the past two decade. http://www.bilkent.edu.tr/~neyapti/shortstudies/012000.pdf
  42. Neyapti B, Berument H (1999) Turkiye Cumhuriyet Merkez Bankasi ne kadar bagimsiz?. Iktisat Isletme ve Finans 14(165): 11–17Google Scholar
  43. Ozer M, Turkyilmaz SA (2005) Time series analysis of inflation and inflation variability in Turkey. J Econ Manag Financ 20: 229Google Scholar
  44. Pederzoli C (2006) Stochastic volatility and GARCH: a comparison based on UK stock data. Eur J Financ 12(1): 41–59CrossRefGoogle Scholar
  45. Ripley B (1987) Stochastic simulation. Wiley, New YorkCrossRefGoogle Scholar
  46. Ruiz E (1994) Quasi-maximum likelihood estimation of stochastic volatility models. J Econ 63: 289–306Google Scholar
  47. Shephard N, Pitt M (1997) Likelihood analysis of non-gaussian measurement time series. Biometrika 84: 653–667CrossRefGoogle Scholar
  48. Taylor SJ (1980) Conjectured models for trend in financial prices, tests and forecasts. J Royal Stat Soc 143(A): 338–362Google Scholar
  49. Taylor SJ (1982) Financial returns modelled by the product of two stochastic processes—a study of daily sugar prices. In: Taylor SJ (eds) Time series analysis: theory and practice 1. North-Holland, Amsterdam, pp 203–226Google Scholar
  50. Telatar F, Telatar E (2003) The relationship between inflation and different sources of inflation uncertainty in Turkey. Appl Econ Lett 10: 431–435CrossRefGoogle Scholar
  51. van Dijk D, Franses PH, Boswijk HP (2007) Absorption of shocks in nonlinear autoregressive models. Comput Stat Data Anal 51: 4206–4226CrossRefGoogle Scholar
  52. Wooldridge JM (1991) On the applications of robust, regression-based diagnostics to models of conditional means and conditional variances. J Econ 47: 5–46Google Scholar
  53. Yao Q, Tong H (1994) Quantifying the influence of initial values on non-linear prediction. J Royal Stat Soc 56(B): 701–725Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • M. Hakan Berument
    • 1
    Email author
  • Yeliz Yalcin
    • 2
  • Julide O. Yildirim
    • 2
  1. 1.Department of EconomicsBilkent UniversityAnkaraTurkey
  2. 2.Department of EconometricsGazi UniversityAnkaraTurkey

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