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Forecasting the stationary AR(1) with an almost unit root

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  • George Halkos
  • Ilias Kevork

Abstract

Although unit root tests have made a great contribution in time series econometrics, their major disadvantage is the low powers they attain on certain occasions, as for the case of the stationary AR(1), when φis close to one. In this study, considering the random walk as the true model, we derive the probability of the prediction interval to include any future value yT+s of AR(1). Using certain estimates from Monte Carlo simulations, we proceed to numerical computations, resulting in the main finding that the values for the specific probability depend upon the location the most recent available observation in the sample possesses in its marginal distribution.

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File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850500407491&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

Volume (Year): 13 (2006)
Issue (Month): 12 ()
Pages: 789-793

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Handle: RePEc:taf:apeclt:v:13:y:2006:i:12:p:789-793

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Cited by:
  1. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
  2. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282.

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