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Dealing with serially correlated errors in the context of spurious regression for two independent stationary AR(1) processes

Author

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  • Christos Agiakloglou
  • Charalampos Agiropoulos

Abstract

Serially correlated errors are most likely to appear in regression analysis when time-series data are used either as a true symptom of autocorrelation or as an indication of a false specification among variables. This study examines the problem of serially correlated errors in the context of spurious regression for two independent stationary AR(1) processes, showing evidence of removing the presence of both symptoms using a Monte Carlo analysis.

Suggested Citation

  • Christos Agiakloglou & Charalampos Agiropoulos, 2022. "Dealing with serially correlated errors in the context of spurious regression for two independent stationary AR(1) processes," Applied Economics Letters, Taylor & Francis Journals, vol. 29(7), pages 619-625, April.
    • Handle: RePEc:taf:apeclt:v:29:y:2022:i:7:p:619-625
    DOI: 10.1080/13504851.2021.1881427
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