## Abstract

Several authors have studied the discrete stochastic process $(x_t)$ in which the $x$'s are related by the stochastic difference equation \begin{equation*}\tag{1.1}x_t = \alpha x_{t - 1} + u_t, \quad t = 1,2, \cdots, T,\end{equation*} where the $u$'s are unobservable disturbances, independent and identically distributed with mean zero and variance $\sigma^2$, and $\alpha$ is an unknown parameter. The statistical problem is to find some appropriate function of the $x$'s as an estimator for $\alpha$ and examine its properties. We may rewrite (1.1) as \begin{equation*}\tag{1.2}x_t = u_t + \alpha u_{t - 1} + \cdots + \alpha^{t - 1}u_1 + \alpha^tx_0.\end{equation*} From (1.2) we see that the distribution of the successive $x$'s is not uniquely determined by that of the $u$'s alone. The distribution of $x_0$ must also be specified. Three distributions which have been proposed for $x_0$ are the following: (A) $x_0$ = a constant (with probability one), (B) $x_0$ is normally distributed with mean zero and variance $\sigma^2/(1 - \alpha^2)$, (C) $x_0 = x_T$. Distribution (B) is perhaps the most appealing from a physical point of view, since if $x_0$ has this distribution and if the $u$'s are normally distributed, then the process is stationary (e.g., see Koopmans [4]). However, there are several analytic difficulties which arise in the statistical treatment of this process. Distribution (C), the so-called circular distribution, has been proposed as an approximation to (B) and is much easier to analyze (e.g., see Dixon [2]). Distribution (A) has been studied extensively by Mann and Wald [5]. An interesting feature of distribution (A) is that $\alpha$ may assume any finite value, while for distributions (B) and (C) $\alpha$ must be between $-1$ and 1. From (1.2) we see that a process satisfying (1.1) and (A) has \begin{equation*}\tag{1.3}\operatorname{var}(x_t) = \sigma^2(1 + \alpha^2 + \cdots + \alpha^{2(t - 1)})\end{equation*} If $|\alpha| \geqq 1, \lim_{t = \infty} \operatorname{var}(x_t) = \infty$ and the process is said to be "explosive." Mann and Wald [5] considered only the case $|\alpha| < 1$. They showed that the least squares estimator for $\alpha$ is the serial correlation coefficient \begin{equation*}\tag{1.4}\hat\alpha = \frac{\sum x_t x_{t - 1}}{\sum x^2_{t - 1}}\end{equation*} and that (for $|\alpha| < 1$) this estimator is asymptotically normally distributed with mean $\alpha$ and variance $(1 - \alpha^2)/T$. Rubin [6] showed that the estimator $\hat\alpha$ is consistent (i.e., $\operatorname{plim} \hat\alpha = \alpha$) for all $\alpha$. In this paper the asymptotic distribution of $\hat\alpha$ will be studied under the assumption that the $u$'s are normally distributed. For $|\alpha| > 1$, it is shown that the asymptotic distribution of $\alpha$ is the Cauchy distribution. For $|\alpha| = 1$, a moment generating function is found, the inversion of which will yield the asymptotic distribution.

## Citation

John S. White. "The Limiting Distribution of the Serial Correlation Coefficient in the Explosive Case." Ann. Math. Statist. 29 (4) 1188 - 1197, December, 1958. https://doi.org/10.1214/aoms/1177706450

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