Politická ekonomie 2006, 54(1):48-55 | DOI: 10.18267/j.polek.545

Model nepozorovaných komponent a jeho využití při identifikaci společných trendů časových řad

Josef Arlt, Petr Pokorný
Vysoká škola ekonomická v Praze.

The model of unobservable components and its use for identification of time series common trends

The co-integration of time series indicates the presence of their common trends. For analytical purpose it is important to transform some time series into gap form. This transformation can be received as a difference between the time series and the common trends. The model of demand for money in Czech Republic created by real M2, real GDP and 1R PRIBOR time series contains two common trends. These trends are estimated from the state space form of unobservable components model. The gap transformations of real M2 and real GDP series can be used for identification of inflation risks. The relatively high correlation was found between gap form of M2 and rate of inflation. The relationship between gap form of GDP and rate of inflation is not so close.

Keywords: inflation, common trends, model of unobservable components, state-space, gap
JEL classification: C32, E31, E41

Published: February 1, 2006  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Arlt, J., & Pokorný, P. (2006). The model of unobservable components and its use for identification of time series common trends. Politická ekonomie54(1), 48-55. doi: 10.18267/j.polek.545
Download citation

References

  1. Arlt, J.: Moderní metody modelování ekonomických ČAsových řad. Grada Publishing, Praha 1999.
  2. Arlt, J., Guba, M., Radkovský, Š., Sojka, M., Stiller, V.: Selected Factors Influencing the Money Demand Development in the Czech Republic in 1994 - 2000. Prague Economic Papers, 2002, č. 1, s. 39-56. Go to original source...
  3. Arlt, J., Guba, M., Radkovský, Š.: Využití metody peněžního převisu/deficitu k indikaci inflačních rizik. Politická ekonomie, 2004, č. 2, s. 183-189.
  4. Beveridge, S., Nelson, C. R.: A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle. Journal of Monetary Economic, 1981, č. 7, s. 151-174. Go to original source...
  5. Durbin, J., Koopman, S. J.: Time Series Analysis by State Space Methods. Oxford, Oxford University Press 2001.
  6. Galí, et al.: European Inflation Dynamics. European Economic Review, 2001, č. 7, s.1237-1270. Galí, J., Gertler, M.: Inflation Dynamics: A Structural Econometric Analysis. Journal of Monetary Economis, 1999, č. 44, s. 195-222. Go to original source...
  7. Garratt, A., Pierse, R. G.: Common Stochastic Trends, Cycles and Sectoral Fluctuations: A Study of Output in the UK. Proceedings of the Seventh World Congress of the Econometric Society in Tokyo 1996.
  8. Harvey, A. C.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge, UK, Cambridge University Press 1989. Go to original source...
  9. Sbordone, A. M.: An Optimizing Model of U.S. Wage and Price Dynamics. Rutgers, The State University of new Jersey, mimeo 2001.
  10. Stock, J. H., Watson, M. W.: Variable Trends in Economic Time Series. Journal of Economic Perspectives, 1988, č. 2, s.147-174. Go to original source...
  11. Vahid, F., Engle, R. F.: Common Trends and Common Cycles. Journal of Applied Econometrics, 1993, č. 8, s. 341-360.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.