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Autoregressive and Moving-average Time-series Processes

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Time Series and Statistics

Part of the book series: The New Palgrave ((NPA))

Abstract

Characterization of time series by means of autoregressive (AR) or moving-average (MA) processes or combined autoregressive moving-average (ARMA) processes was suggested, more or less simultaneously, by the Russian statistician and economist, E. Slutsky (1927), and the British statistician G.U. Yule (1921, 1926, 1927). Slutsky and Yule observed that if we begin with a series of purely random numbers and then take sums or differences, weighted or unweighted, of such numbers, the new series so produced has many of the apparent cyclic properties that are thought to characterize economic and other time series. Such sums or differences of purely random numbers are the basis for ARMA models of the processes by which many kinds of economic time series are assumed to be generated, and thus form the basis for recent suggestions for analysis, forecasting and control (e.g. Box and Jenkins, 1970).

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Authors
  1. Marc Nerlove
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  2. Francis X. Diebold
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Editor information

John Eatwell Murray Milgate Peter Newman

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© 1990 Palgrave Macmillan, a division of Macmillan Publishers Limited

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Nerlove, M., Diebold, F.X. (1990). Autoregressive and Moving-average Time-series Processes. In: Eatwell, J., Milgate, M., Newman, P. (eds) Time Series and Statistics. The New Palgrave. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-20865-4_3

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  • DOI: https://doi.org/10.1007/978-1-349-20865-4_3

  • Publisher Name: Palgrave Macmillan, London

  • Print ISBN: 978-0-333-49551-3

  • Online ISBN: 978-1-349-20865-4

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