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Testing for trends in correlated data

  • Sun, Hongguang
  • Pantula, Sastry G.
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    The problem of testing for the significance of a linear trend in the presence of positively correlated errors is considered. Test criteria based on ordinary least squares, conditional maximum likelihood, estimated generalized least squares and maximum likelihood estimates tend to have higher significance levels than nominal levels for positively correlated series of moderate length. In this paper, we study three alternative methods: (a) pre-test, (b) bias-adjusted, and (c) bootstrap-based procedures. A simulation study is used to compare the empirical level and power of different procedures. An example is used to illustrate the procedures.

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    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 41 (1999)
    Issue (Month): 1 (January)
    Pages: 87-95

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    Handle: RePEc:eee:stapro:v:41:y:1999:i:1:p:87-95
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    1. Park, Rolla Edward & Mitchell, Bridger M., 1980. "Estimating the autocorrelated error model with trended data," Journal of Econometrics, Elsevier, vol. 13(2), pages 185-201, June.
    2. Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-54, November.
    3. Charles R. Nelson & Heejoon Kang, 1983. "Pitfalls in the use of Time as an Explanatory Variable in Regression," NBER Technical Working Papers 0030, National Bureau of Economic Research, Inc.
    4. Beach, Charles M & MacKinnon, James G, 1978. "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors," Econometrica, Econometric Society, vol. 46(1), pages 51-58, January.
    5. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
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