Interaction between autocorrelation and conditional heteroscedasticity: A random-coefficient approach

Anil K. Bera, Matthew L. Higgins, Sangkyu Lee

Research output: Contribution to journalArticlepeer-review


In applied econometrics, we tend to tackle specification problems one at a time rather than considering them jointly. This has serious consequences for statistical inference. One example of this is considering autocorrelation and autoregressive conditional heteroscedasticity (ARCH) separately. In this article we consider a linear regression model with random coefficient autoregressive disturbances that provides a convenient framework to analyze autocorrelation and ARCH simultaneously. Our stationarity conditions and testing results reveal the strong interaction between ARCH and autocorrelation. An empirical example of testing the unbiasedness of experts’ expectations of inflation demonstrates that neglecting conditional heteroscedasticity or misspecifying the autocorrelation structure might result in unreliable inference.

Original languageEnglish (US)
Pages (from-to)133-142
Number of pages10
JournalJournal of Business and Economic Statistics
Issue number2
StatePublished - Apr 1992


  • Lagrange multiplier test
  • Livingston biannual survey data
  • Price expectation
  • Stationarity condition Unbiasedness hypothesis

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Interaction between autocorrelation and conditional heteroscedasticity: A random-coefficient approach'. Together they form a unique fingerprint.

Cite this