Estimating deterministic trends with an integrated or stationary noise component
書誌事項
- 公開日
- 2009-07
- 権利情報
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- https://www.elsevier.com/tdm/userlicense/1.0/
- DOI
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- 10.1016/j.jeconom.2009.03.011
- 公開者
- Elsevier BV
この論文をさがす
説明
Abstract We propose a test for the slope of a trend function when it is a priori unknown whether the series is trend-stationary or contains an autoregressive unit root. The procedure is based on a Feasible Quasi Generalized Least Squares method from an AR(1) specification with parameter α , the sum of the autoregressive coefficients. The estimate of α is the OLS estimate obtained from an autoregression applied to detrended data and is truncated to take a value 1 whenever the estimate is in a T − δ neighborhood of 1. This makes the estimate “super-efficient” when α = 1 and implies that inference on the slope parameter can be performed using the standard Normal distribution whether α = 1 or | α | 1 . Theoretical arguments and simulation evidence show that δ = 1 / 2 is the appropriate choice. Simulations show that our procedure has better size and power properties than the tests proposed by [Bunzel, H., Vogelsang, T.J., 2005. Powerful trend function tests that are robust to strong serial correlation with an application to the Prebish–Singer hypothesis. Journal of Business and Economic Statistics 23, 381–394] and [Harvey, D.I., Leybourne, S.J., Taylor, A.M.R., 2007. A simple, robust and powerful test of the trend hypothesis. Journal of Econometrics 141, 1302–1330].
収録刊行物
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- Journal of Econometrics
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Journal of Econometrics 151 (1), 56-69, 2009-07
Elsevier BV
