摘要：The present paper obtains a complete description of the limit distributions of sample covariances in $N\times n$ panel data when $N$ and $n$ jointly increase, possibly at different rate. The panel is formed by $N$ independent samples of length $n$ from random-coefficient AR(1) process with the tail distribution function of the random coefficient regularly varying at the unit root with exponent $\beta >0$. We show that for $\beta\in (0,2)$ the sample covariances may display a variety of stable and non-stable limit behaviors with stability parameter depending on $\beta$ and the mutual increase rate of $N$ and $n$.
关键词：Autoregressive model; panel data; mixture distribution; long memory; sample covariance; scaling transition; Poisson random measure; asymptotic self-similarity
