Interaction between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach.

In applied econometrics, the authors 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 separately. In this article, the authors consider a linear regression model with random coefficient autoregressive disturbances that provides a convenient framework to analyze autocorrelation and autoregressive conditional heteroscedasticity simultaneously. Their stationarity conditions and testing results reveal the strong interaction between autoregressive conditional heteroscedasticity 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.