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ALTERNATIVE ESTIMATORS AND UNIT ROOT TESTS FOR THE AUTOREGRESSIVE PROCESS
Abstract
Abstract. We compare several estimators for the second‐order autoregressive process and compare the associated tests for a unit root. Monte Carlo results are reported for the ordinary least squares estimator, the simple symmetric least squares estimator and the weighted symmetric least squares estimator. The weighted symmetric least squares estimator of the autoregressive parameters generally has smaller mean square error than that of the ordinary least squares estimator, particularly when one root is close to one in absolute value. For the second‐order model with known zero intercept, the one‐sided ordinary least squares test for a unit root is more powerful than the symmetric tests. For the model with an estimated intercept, the one‐sided weighted symmetric least squares test is the most powerful test.
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Citations
Cited by
A simple panel unit root test in the presence of cross‐section dependence
Pesaran, M. H. (2007) · Journal of Applied Econometrics · doi:10.1002/jae.951 · Cited by 12,787
A pair-wise approach to testing for output and growth convergence
Hashem Pesaran, M. (2007) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.05.024 · Cited by 343
Metadata
- Authors
- Park, H. J., & Fuller, W. A.
- Venue
- Journal of Time Series Analysis
- Publisher
- Wiley
- Published
- 1995-07
- Volume
- 16
- Issue
- 4
- Pages
- 415-429
- ISSN
- 0143-9782 (ISSN-L)
- 1467-9892
- OpenAlex
- W2055648209
- Cited by
- 254
- References
- 0
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: ALTERNATIVE ESTIMATORS AND UNIT ROOT TESTS FOR THE AUTOREGRESSIVE PROCESS. https://app.cochat.ai/papers/10.1111/j.1467-9892.1995.tb00243.x
- The paper
- Park, H. J., & Fuller, W. A. (1995). ALTERNATIVE ESTIMATORS AND UNIT ROOT TESTS FOR THE AUTOREGRESSIVE PROCESS. Journal of Time Series Analysis. https://doi.org/10.1111/j.1467-9892.1995.tb00243.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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