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Generalized linear autoregressions

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  • Neil Shephard

Abstract

This paper develops a class of autoregressive and moving average models which extend the generalized linear model. Likelihood and quasi-likelihood estimation procedures are developed which allow the models to be easily estimated and tested. Several examples are given which illustrate the usefulness and simplicity of the approach advocated in this paper.

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File URL: http://www.nuff.ox.ac.uk/economics_wp/w8/glar.zip
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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 8..

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Date of creation: Feb 1995
Date of revision:
Handle: RePEc:nuf:econwp:0008

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Web page: http://www.nuff.ox.ac.uk/economics/

Related research

Keywords: ARCH; autoregression; binomial; categorical data; count data; diagnostic checking; exponential family; gamma; generalized linear models; martingale difference; moving average; overdispersion; poisson.;

References

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  1. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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Citations

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Cited by:
  1. Robert Jung & A. Tremayne, 2011. "Useful models for time series of counts or simply wrong ones?," AStA Advances in Statistical Analysis, Springer, Springer, vol. 95(1), pages 59-91, March.
  2. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, Elsevier, vol. 78(17), pages 2963-2970, December.
  3. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2011. "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 26(4), pages 669-707, 06.
  4. Jeffrey R. Russell & Robert F. Engle, 1998. "Econometric Analysis of Discrete-valued Irregularly-spaced Financial Transactions Data Using a New Autoregressive Conditional Multinomial Model," CRSP working papers, Center for Research in Security Prices, Graduate School of Business, University of Chicago 470, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  5. Drew Creal & Siem Jan Koopman & Andre Lucas, 2009. "A General Framework for Observation Driven Time-Varying Parameter Models," Global COE Hi-Stat Discussion Paper Series, Institute of Economic Research, Hitotsubashi University gd08-038, Institute of Economic Research, Hitotsubashi University.
  6. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, Elsevier, vol. 28(2), pages 485-496.
  7. Drew Creal & Siem Jan Koopman & Andr� Lucas, 2008. "A General Framework for Observation Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers, Tinbergen Institute 08-108/4, Tinbergen Institute.
  8. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 4/07, Monash University, Department of Econometrics and Business Statistics.
  9. Fabrizio Cipollini & Robert F. Engle & Giampiero Gallo, 2006. "Vector Multiplicative Error Models: Representation and Inference," Econometrics Working Papers Archive, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" wp2006_15, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  10. Jung, Robert & Kukuk, Martin & Liesenfeld, Roman, 2005. "Time Series of Count Data : Modelling and Estimation," Economics Working Papers, Christian-Albrechts-University of Kiel, Department of Economics 2005,08, Christian-Albrechts-University of Kiel, Department of Economics.

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