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Estimating Deterministic Trends with an Integrated or Stationary Noise Component

Author

Listed:
  • Pierre Perron

    (Department of Economics, Boston University)

  • Tomoyoshi Yabu

    (Department of Economics, Boston University)

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 [alpha], the sum of the autoregressive coefficients. The estimate of [alpha] 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-[delta] neighborhood of 1. This makes the estimate "super-efficient" when [alpha]=1 and implies that inference on the slope parameter can be performed using the standard Normal distribution whether [alpha]=1 or [alpha]
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Suggested Citation

  • Pierre Perron & Tomoyoshi Yabu, "undated". "Estimating Deterministic Trends with an Integrated or Stationary Noise Component," Boston University - Department of Economics - Working Papers Series WP2006-012, Boston University - Department of Economics, revised Feb 2006.
    • Handle: RePEc:bos:wpaper:wp2006-012
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    More about this item

    Keywords

    linear trend; unit root; median-unbiased estimates; GLS procedure; super efficient estimates;
    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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