Asymptotic inference for AR(1) processes with (nonnormal) stable errors

This is the first of several papers in which we consider problems related to the asymptotic distribution of the least squares estimate of the parameter γ in theAR(1) model $$X_k = \gamma X_{k - 1} + \varepsilon _k , k = 1,...,n,$$ where ε_k are independent identically distributed (i.i.d.) random variables in the domain of attraction of a stable law. In §1 we give a summary in the case ε_k is in the domain of attraction of the normal distribution. In §2 we consider errors in the domain of attraction of a (nonnormal) stable distribution. In §3 we prove a result in the case of the completely asymmetric stable distribution with α=β=1.     

Asymptotic inference for AR(1) panel data

A general asymptotic theory is given for the panel data AR(1) model with time series independent in different cross sections. The theory covers the cases of stationary process, local to unity process, unit root process, mildly integrated, mildly explosive and explosive processes. It is assumed that the cross-sectional dimension and time-series dimension are respectively N and T. The results in this paper illustrate that whichever the process is, with an appropriate regularization, the least squares estimator of the autoregressive coefficient converges in distribution to a normal distribution with rate at least O(N-1/3). Since the variance is the key to characterize the normal distribution, it is important to discuss the variance of the least squares estimator. We will show that when the autoregressive coefficient ρ satisfies |ρ| 1, the variance declines at the rate O((NT)-1), while the rate changes to O(N~(-1) T~(-2)) when ρ = 1 and O(N~(-1)ρ~(-2 T+4)) when |ρ| 1. ρ = 1 is the critical point where the convergence rate changes radically. The transition process is studied by assuming ρ depending on T and going to 1. An interesting phenomenon discovered in this paper is that, in the explosive case, the least squares estimator of the autoregressive coefficient has a standard normal limiting distribution in the panel data case while it may not has a limiting distribution in the univariate time series case.     

A note on self-normalized Dickey-Fuller test for unit root in autoregressive time series with GARCH errors

In this article, the unit root test for AR（p） model with GARCH errors is considered. The Dickey-Fuller test statistics are rewritten in the form of self-normalized sums, and the asymptotic distribution of the test statistics is derived under the weak conditions.     

A note on asymptotic inference in a class of non-stationary processes

In this note some problems of asymptotic inference in a class of non-stationary stochastic processes are considered. In particular, it is shown that no criterion based on the existence of uniformly most powerful tests over a local neighborhood can be used in this situation.     

A note on the normalizing sequences for sums of linear processes in the case of negative memory

We consider the partial-sum process \( {S}_n(t)={\sum}_{k=0}^{\left\lfloor nt\right\rfloor }{X}_k \) of linear processes \( {X}_n={\sum}_{i=0}^{\infty }{c}_i{\upxi}_{n-i} \) with independent identically distributed innovations {ξ _i } belonging to the domain of attraction of α-stable law (0 < α ≤ 2). If |c _k |?=?k ^?γ?,?k?∈???,?γ?> max(1, 1/α), and \( {\sum}_{k=0}^{\infty}\kern0.5em ck=0 \) (the case of negative memory for the stationary sequence {X _n }), then it is known that the normalizing sequence of S _n (1) can grow as n ^1/α?γ+1 or remain bounded if the signs of the coefficients are constant or alternate, respectively. It is of interest to know whether it is possible, given ? ∈ (0, 1/α ? γ + 1), to change the signs of c _k so that the rate of growth of the normalizing sequence would be n ^? . In this paper, we give the positive answer: we propose a way of choosing the signs and investigate the finite-dimensional convergence of appropriately normalized S _n (t) to linear fractional Lévy motion.     

固定设计下非参数回归模型估计的渐近性质

考虑固定设计下具有非参数AR(1)的非参数回归模型,综合最小二乘和非参数核估计法,定义了非参数函数的估计量,在适当的条件下,研究了它们的渐近性质.     

一个简化的新Laplace AR(1)模型 参数估计及其渐近性质

研究了一个简化的新的Laplace AR(1)模型参数的条件最小二乘估计和最大拟似然估计,并讨论了它们的强相合性和渐近正态性.通过数值模拟和实际例子,说明了最大拟似然估计及模型的优越性.     

Asymptotic behaviour of the least squares estimator of the mean of AR(1) models

The aim of the paper is to investigate the limit behaviour of the least squares estimator of the shift parameter of nearly unstable, nearly stable, and nearly explosive AR(1) models. Both zero start and stationary cases are treated. Connection with the maximum likelihood estimator of the shift parameter of continuous time AR(1) processes is also discussed.     

A Unit Root Test for an AR(1) Process with AR Errors by Using Random Weighted Bootstrap

A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root test.In this paper,we consider the unit root testing problem with errors being long-memory processes with the LARCH structure.A new test statistic is developed by using the random weighted bootstrap method.It turns out that the proposed statistic has a chisquared distribution asymptotically regardless of the process being stationary or nonst at ionary,and with ...     

A note on statistical inference for a class of diffusions and approximate diffusions

Some optimal inference results for a class of diffusion processes, including the continuous state branching process and the approximate Wright-Fisher model with selection, are derived.It is then showed how the theory of convergence of experiments, due to Le Cam, can be applied to derive corresponding results for processes approximating these diffusions.     

A note on the Selberg class (degrees 0 and 1)

We give a classification of the Dirichlet polynomials in the Selberg class as entire quotients of Dirichlet series with periodic coefficients and the Riemann zeta-function. Received: 2 November 2001     

An estimator for parameters of a nonlinear nonnegative multidimensional AR(1) process

Let e _t=(e _t1,...e _tp) be a p-dimensional nonnegative strict white noise with finite second moments. Let h _ij(x) be nondecreasing functions from [0,) onto [0,) such that h _ij(x) x for i, j = 1,...,p. Let U = (u _ij) be a p×p matrix with nonnegative elements having all its roots inside the unit circle. Define a process X t=(X _t1,...,X _tp) by for

A note on estimating the number of errors in a system by recapture sampling

Some distributional results related to the recapture debugging experiment of Nayak (1988) for estimating the number of errors, N, in a software are obtained. It is proved that while the sufficient statistics are complete, an unbiased estimator of N based on them does not exist.