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Beauty and Ugliness of Aggregation over Time: A Survey

Author

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  • Mamingi Nlandu

    (Department of Economics, The University of the West Indies, P.O.Box 64, Cave Hill Campus, BridgetownBB11000, Barbados)

Abstract

This paper delivers an up-to-date literature review dealing with aggregation over time of economic time series, e.g. the transformation of high-frequency data to low frequency data, with a focus on its benefits (the beauty) and its costs (the ugliness). While there are some benefits associated with aggregating data over time, the negative effects are numerous. Aggregation over time is shown to have implications for inferences, public policy and forecasting.

Suggested Citation

  • Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    • Handle: RePEc:lus:reveco:v:68:y:2017:i:3:p:205-227:n:3
    DOI: 10.1515/roe-2017-0027
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    1. Lahiri, Kajal & Mamingi, Nlandu, 1995. "Testing for cointegration: Power versus frequency of observation -- another view," Economics Letters, Elsevier, vol. 49(2), pages 121-124, August.
    2. Christiano, Lawrence J. & Eichenbaum, Martin, 1987. "Temporal aggregation and structural inference in macroeconomics," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 26(1), pages 63-130, January.
    3. Hakkio, Craig S. & Rush, Mark, 1991. "Cointegration: how short is the long run?," Journal of International Money and Finance, Elsevier, vol. 10(4), pages 571-581, December.
    4. Shiller, Robert J. & Perron, Pierre, 1985. "Testing the random walk hypothesis : Power versus frequency of observation," Economics Letters, Elsevier, vol. 18(4), pages 381-386.
    5. Wei, William W. S., 1978. "The effect of temporal aggregation on parameter estimation in distributed lag model," Journal of Econometrics, Elsevier, vol. 8(2), pages 237-246, October.
    6. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    7. Stock], James H., 1987. "Temporal aggregation and structural inference in macroeconomics a comment," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 26(1), pages 131-139, January.
    8. Zellner, Arnold & Montmarquette, Claude, 1971. "A Study of Some Aspects of Temporal Aggregation Problems in Econometric Analyses," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 335-342, November.
    9. Moriguchi, C, 1970. "Aggregation over Time in Macroeconomic Relations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 11(3), pages 427-440, October.
    10. Bergstrom, A.R., 1984. "Continuous time stochastic models and issues of aggregation over time," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 20, pages 1145-1212, Elsevier.
    11. Perron, Pierre, 1991. "Test Consistency with Varying Sampling Frequency," Econometric Theory, Cambridge University Press, vol. 7(3), pages 341-368, September.
    12. Rossana, Robert J & Seater, John J, 1995. "Temporal Aggregation and Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 441-451, October.
    13. Chambers, Marcus J., 2005. "The purchasing power parity puzzle, temporal aggregation, and half-life estimation," Economics Letters, Elsevier, vol. 86(2), pages 193-198, February.
    14. Geweke, John F, 1978. "Temporal Aggregation in the Multiple Regression Model," Econometrica, Econometric Society, vol. 46(3), pages 643-661, May.
    15. Hafner, Christian M., 2008. "Temporal aggregation of multivariate GARCH processes," Journal of Econometrics, Elsevier, vol. 142(1), pages 467-483, January.
    16. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    17. Pierse, R. G. & Snell, A. J., 1995. "Temporal aggregation and the power of tests for a unit root," Journal of Econometrics, Elsevier, vol. 65(2), pages 333-345, February.
    18. Robert F. Engle & Ta-Chung Liu, 1972. "Effects of Aggregation Over Time on Dynamic Characteristics of an Econometric Model," NBER Chapters, in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 673-737, National Bureau of Economic Research, Inc.
    19. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 369-380, October.
    20. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    21. Daniel O. Stram & William W. S. Wei, 1986. "Temporal Aggregation In The Arima Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(4), pages 279-292, July.
    22. Granger, C. W. J. & Siklos, Pierre L., 1995. "Systematic sampling, temporal aggregation, seasonal adjustment, and cointegration theory and evidence," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 357-369.
    23. 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.
    24. Palm, F. C. & Nijman, T. E., 1982. "Linear regression using both temporally aggregated and temporally disaggregated data," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 333-343, August.
    25. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    26. Hsiao, Cheng, 1979. "Linear regression using both temporally aggregated and temporally disaggregated data," Journal of Econometrics, Elsevier, vol. 10(2), pages 243-252, June.
    27. Campos, Julia & Ericsson, Neil R. & Hendry, David F., 1990. "An analogue model of phase-averaging procedures," Journal of Econometrics, Elsevier, vol. 43(3), pages 275-292, March.
    28. Jörg Breitung & Norman R. Swanson, 2002. "Temporal aggregation and spurious instantaneous causality in multiple time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(6), pages 651-665, November.
    29. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-395, October.
    30. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    31. Fotios Petropoulos & Nikolaos Kourentzes, 2014. "Improving Forecasting via Multiple Temporal Aggregation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 34, pages 12-17, Summer.
    32. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    33. Yoel Haitovsky & George Treyz & Vincent Su, 1974. "Forecasts with Quarterly Macroeconomic Models," NBER Books, National Bureau of Economic Research, Inc, number hait74-1, October.
    34. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    35. Lippi, Marco & Reichlin, Lucrezia, 1991. "Trend-Cycle Decompositions and Measures of Persistence: Does Time Aggregation Matter?," Economic Journal, Royal Economic Society, vol. 101(405), pages 314-323, March.
    36. In Choi & Bhum Suk Chung, 1995. "Sampling frequency and the power of tests for a unit root: A simulation study," Economics Letters, Elsevier, vol. 49(2), pages 131-136, August.
    37. Alfred A. Haug, 2002. "Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 399-412, September.
    38. Hendry, David F., 1992. "An econometric analysis of TV advertising expenditure in the United Kingdom," Journal of Policy Modeling, Elsevier, vol. 14(3), pages 281-311, June.
    39. Rajaguru, Gulasekaran & Abeysinghe, Tilak, 2008. "Temporal aggregation, cointegration and causality inference," Economics Letters, Elsevier, vol. 101(3), pages 223-226, December.
    40. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    41. Mamingi, Nlandu, 1996. "Aggregation over time, error correction models and Granger causality: A Monte Carlo investigation," Economics Letters, Elsevier, vol. 52(1), pages 7-14, July.
    42. Phillips, P C B, 1991. "Error Correction and Long-Run Equilibrium in Continuous Time," Econometrica, Econometric Society, vol. 59(4), pages 967-980, July.
    43. Cunningham, Steven R. & Vilasuso, Jon R., 1997. "Time Aggregation and the Money-Real GDP Relationship," Journal of Macroeconomics, Elsevier, vol. 19(4), pages 675-695, October.
    44. Sims, Christopher A, 1971. "Discrete Approximations to Continuous Time Distributed Lags in Econometrics," Econometrica, Econometric Society, vol. 39(3), pages 545-563, May.
    45. 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.
    46. Hall, Alastair R, 1994. "Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 461-470, October.
    47. Hooker, Mark A., 1993. "Testing for cointegration : Power versus frequency of observation," Economics Letters, Elsevier, vol. 41(4), pages 359-362.
    48. Teles, Paulo & Wei, William W. S., 2000. "The effects of temporal aggregation on tests of linearity of a time series," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 91-103, July.
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    More about this item

    Keywords

    aggregation over time; temporal aggregation; systematic sampling; mixed aggregation;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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