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On the effects of aggregating cointegrated variables over time

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  • Müller, Christian

Abstract

It has long been recognized that aggregating time series introduces correlation between consecutive values of the aggregated observations (see Working (1960)). This paper investigates the effect of aggregation on the relation between variables assuming that the data generating process involves two integrated variables linked by a specific error correction mechanism (cointegration). It will be shown that aggregation does not distort the cointegration relation while some other features of the data generating process will change considerably. Cointegration tests become invalid in a single equation framework but system cointegration analysis seems to be robust against various aggregation strategies.

Suggested Citation

  • Müller, Christian, 2002. "On the effects of aggregating cointegrated variables over time," SFB 373 Discussion Papers 2002,9, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  • Handle: RePEc:zbw:sfb373:20029
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    References listed on IDEAS

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    1. J. FAN & Wolfgang HÄRDLE & Enno MAMMEN, 1996. "Direct estimation of low dimensional components in additive models," SFB 373 Discussion Papers 1996,17, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Masry, Elias & Tjøstheim, Dag, 1997. "Additive Nonlinear ARX Time Series and Projection Estimates," Econometric Theory, Cambridge University Press, vol. 13(02), pages 214-252, April.
    3. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    4. Sperlich, Stefan & Tj stheim, Dag & Yang, Lijian, 2002. "Nonparametric Estimation And Testing Of Interaction In Additive Models," Econometric Theory, Cambridge University Press, vol. 18(02), pages 197-251, April.
    5. Franses, Philip Hans, 1996. "Periodicity and Stochastic Trends in Economic Time Series," OUP Catalogue, Oxford University Press, number 9780198774549.
    6. O. B. LINTON & Wolfgang HÄRDLE, 1995. "Estimation of Additive Regression Models with Links," SFB 373 Discussion Papers 1995,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. Oliver Linton & E. Mammen & J. Nielsen, 1997. "The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions," Cowles Foundation Discussion Papers 1160, Cowles Foundation for Research in Economics, Yale University.
    8. Yang, Lijian & Tschernig, Rolf, 2002. "Non- And Semiparametric Identification Of Seasonal Nonlinear Autoregression Models," Econometric Theory, Cambridge University Press, vol. 18(06), pages 1408-1448, December.
    9. Jürgen WOLTERS, 1992. "Persistence and Seasonality in output and Employment of the Federal Republic of Germany," Discussion Papers (REL - Recherches Economiques de Louvain) 1992043, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    10. repec:cup:etheor:v:13:y:1997:i:2:p:214-52 is not listed on IDEAS
    11. Masry, Elias & Tjøstheim, Dag, 1995. "Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality," Econometric Theory, Cambridge University Press, vol. 11(02), pages 258-289, February.
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    More about this item

    Keywords

    cointegration; aggregation; time series;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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