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Modelling memory of economic and financial time series

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  • Robinson, Peter

Abstract

Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of ‘memory’, or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.

Suggested Citation

  • Robinson, Peter, 2005. "Modelling memory of economic and financial time series," LSE Research Online Documents on Economics 2069, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:2069
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    File URL: http://eprints.lse.ac.uk/2069/
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    References listed on IDEAS

    as
    1. Robinson, Peter M., 1977. "The construction and estimation of continuous time models and discrete approximations in econometrics," Journal of Econometrics, Elsevier, vol. 6(2), pages 173-197, September.
    2. Peter M Robinson, 2001. "The Memory of Stochastic Volatility Models," STICERD - Econometrics Paper Series 410, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Robinson, P. M., 1991. "Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression," Journal of Econometrics, Elsevier, vol. 47(1), pages 67-84, January.
    4. Robinson, P. M., 2001. "The memory of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 101(2), pages 195-218, April.
    5. Robinson, Peter M., 2001. "The memory of stochastic volatility models," LSE Research Online Documents on Economics 2298, London School of Economics and Political Science, LSE Library.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Yaya, OlaOluwa S & Ogbonna, Ephraim A & Furuoka, Fumitaka & Gil-Alana, Luis A., 2019. "A new unit root analysis for testing hysteresis in unemployment," MPRA Paper 96621, University Library of Munich, Germany.

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    More about this item

    Keywords

    Long memory; short memory; stochastic volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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