Modelling of Economic Time Series and the Method of Cointegration

Authors

  • Jiri Neubauer University of Defence, Brno, Czech Republic

DOI:

https://doi.org/10.17713/ajs.v35i2&3.377

Abstract

The article is focused on the problem of modelling multidimensional non-stationary cointegrated processes. It is a modern method especially used for the description of multidimensional economic time series. The multidimensional process Yt is called cointegrated with the cointegrating vector ?, if the process ?´Yt is stationary or trend-stationary. For instance this property can be found in some series of economic indices which are predominantly non-stationary. Methods connected with estimates of cointegrating
vectors and with a cointegration testing are applied to economic data. All the methods given were programmed in the computing environment MATLAB.

References

Banerjee, A., Dolado, J. J., Galbraith, J. W., and Hendry, D. F. (1993). Co-integration, error-correction and the econometric analysis of non-stationary data (1st ed.). Oxford: Oxford University Press.

Engle, R. F., and Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55, 251-276.

Hamilton, J. D. (1994). Time series analysis (1st ed.). Princeton: Princeton University Press.

Johansen, S. (1995). Likelihood-based inference in cointegrated vector auto-regressive models (1st ed.). Oxford: Oxford University Press.

Neubauer, J. (2004). Method of cointegration and exchange rates. Summer School Datastat03, Proceedings, Folia Fac. Sci. Nat. Univ. Masaryk. Brunensis, Mathematica 15, 15, 241-255.

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Published

2016-04-03

How to Cite

Neubauer, J. (2016). Modelling of Economic Time Series and the Method of Cointegration. Austrian Journal of Statistics, 35(2&3), 307–313. https://doi.org/10.17713/ajs.v35i2&3.377

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Articles