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Time‐scale transformations of discrete time processes

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  • Òscar Jordà
  • Massimiliano Marcellino

Abstract

. This paper investigates the effects of temporal aggregation when the aggregation frequency is variable and possibly stochastic. The results that we report include, as a particular case, the well‐known results on fixed‐interval aggregation, such as when monthly data are aggregated into quarters. A variable aggregation frequency implies that the aggregated process will exhibit time‐varying parameters and non‐spherical disturbances, even when these characteristics are absent from the original model. Consequently, we develop methods for specification and estimation of the aggregate models and show with an example how these methods perform in practice.

Suggested Citation

  • Òscar Jordà & Massimiliano Marcellino, 2004. "Time‐scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
  • Handle: RePEc:bla:jtsera:v:25:y:2004:i:6:p:873-894
    DOI: 10.1111/j.1467-9892.2004.00383.x
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    References listed on IDEAS

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    1. Jorda, Oscar, 1999. "Random-Time Aggregation in Partial Adjustment Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 382-395, July.
    2. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464.
    3. Stock, James H., 1987. "Measuring Business Cycle Time," Scholarly Articles 3425950, Harvard University Department of Economics.
    4. Hinich, Melvin J., 1999. "Sampling Dynamical Systems," Macroeconomic Dynamics, Cambridge University Press, vol. 3(4), pages 602-609, December.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Hannan, E J, 1971. "The Identification Problem for Multiple Equation Systems with Moving Average Errors," Econometrica, Econometric Society, vol. 39(5), pages 751-765, September.
    7. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    8. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    9. Durland, J Michael & McCurdy, Thomas H, 1994. "Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 279-288, July.
    10. Ricardo J. Caballero & Eduardo M. R. A. Engel, 1993. "Microeconomic Adjustment Hazards and Aggregate Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(2), pages 359-383.
    11. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    12. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    13. Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June.
    14. Lam, Pok-sang, 1990. "The Hamilton model with a general autoregressive component: estimation and comparison with other models of economic time series : Estimation and comparison with other models of economic time series," Journal of Monetary Economics, Elsevier, vol. 26(3), pages 409-432, December.
    15. Stock, James H, 1987. "Measuring Business Cycle Time," Journal of Political Economy, University of Chicago Press, vol. 95(6), pages 1240-1261, December.
    16. Sims, Christopher A, 1971. "Discrete Approximations to Continuous Time Distributed Lags in Econometrics," Econometrica, Econometric Society, vol. 39(3), pages 545-563, May.
    17. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
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    Cited by:

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    2. Shigeru Fujita & Garey Ramey, 2006. "The cyclicality of job loss and hiring," Working Papers 06-17, Federal Reserve Bank of Philadelphia.
    3. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Robert Kunst & Philip Franses, 2015. "Asymmetric time aggregation and its potential benefits for forecasting annual data," Empirical Economics, Springer, vol. 49(1), pages 363-387, August.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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