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Efficient tests for unit roots with prediction errors

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  • Sánchez, Ismael

Abstract

It is well known that the main difference between a stationary (or trend-stationary) process and a process with a unit root is to be observed in their long-term behaviour. This paper exploits this idea and shows that nearly optimal unit-root tests can admit an interpretation based on prediction performance. This result is not only useful in understanding how efficient tests use the information, but it can also be used to construct new unit-root tests based on prediction errors. A Monte Carlo experiment for the autoregressive moving-average of order (1,1) indicates that the proposed tests have desirable size and power properties

Suggested Citation

  • Sánchez, Ismael, 2000. "Efficient tests for unit roots with prediction errors," DES - Working Papers. Statistics and Econometrics. WS 10113, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:10113
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    References listed on IDEAS

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    1. Elliott, Graham, 1999. "Efficient Tests for a Unit Root When the Initial Observation Is Drawn from Its Unconditional Distribution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(3), pages 767-783, August.
    2. Dufour, Jean-Marie & King, Maxwell L., 1991. "Optimal invariant tests for the autocorrelation coefficient in linear regressions with stationary or nonstationary AR(1) errors," Journal of Econometrics, Elsevier, vol. 47(1), pages 115-143, January.
    3. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    4. Alok Bhargava, 1986. "On the Theory of Testing for Unit Roots in Observed Time Series," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(3), pages 369-384.
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    Cited by:

    1. Sánchez, Ismael, 2000. "Spectral density estimators at frequency zero for nonstationarity tests in arma models," DES - Working Papers. Statistics and Econometrics. WS 10132, Universidad Carlos III de Madrid. Departamento de Estadística.

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