Interaction Between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach
提出一个随机系数自回归扰动项的线性回归模型，同时分析自相关和ARCH效应，发现两者存在强交互作用，并通过通胀预期无偏性检验的实证例子说明忽略这种交互会导致不可靠的推断。
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.