We propose a nonstationary periodogram and various parametric approaches for estimating the spectral density of a nonstationary spatial process. We also study the asymptotic properties of the proposed estimators via shrinking asymptotics, assuming the distance between neighbouring observations tends to zero as the size of the observation region grows without bound. With this type of asymptotic ...

Relative prices are nonstationary and standard root-T inference is invalid for demand systems. But demand systems are nonlinear functions of relative prices, and standard methods for dealing with nonstationarity in linear models cannot be used. Demand system residuals are also frequently found to be highly persistent, further complicating estimation and inference. We propose a variant of the tr...

Simulation models of real-life systems often assume stationary (homogeneous) Poisson arrivals. Therefore, when nonstationary arrival processes are required it is natural to assume Poisson arrivals with a time-varying arrival rate. For many systems, however, this provides an inaccurate representation of the arrival process which is either more or less variable than Poisson. In this paper we exte...

This paper explores the notion and usefulness of nonstationary velocity profiles for high priority emergency vehicle transit on automated highways. These profiles are intended for use in the link layer of the hierarchical control architecture defined by the California Partners for Automated Transit and Highways (PATH) program. A non-stationary velocity profile which can be used to circulate tra...

In this paper, we consider an adaptive base-stock policy for a single-item inventory system, where the demand process is nonstationary. In particular, the demand process is an integrated moving average process of order (0, 1, 1), for which an exponential-weighted moving average provides the optimal forecast. For the assumed control policy we characterize the inventory random variable and use th...

We present a general framework for systems which are prepared in a nonstationary nonequilibrium state in the absence of any perturbation and which are then further driven through the application of a time-dependent perturbation. By assumption, the evolution of the system must be described by Markovian dynamics. We distinguish two different situations depending on the way the nonequilibrium stat...

We develop a method for constructing prediction intervals nonstationary variable, such as GDP. The uses Factor Augmented Regression (FAR) model. predictors in the model include small number of factors generated to extract most information set panel data on large macroeconomic variables that are considered be potential predictors. novelty this article is it provides and justification mixture sta...

This paper develops an asymptotic theory for time series discrete choice models with explanatory variables generated as integrated processes and with multiple choices and threshold parameters determining the choices. The theory extends recent work by Park and Phillips (2000) on binary choice models. As in this earlier work, the maximum likelihood (ML) estimator is consistent and has a limit the...

We develop a model of nonstationary relational contracts in order to study internal wage dynamics. Workers are heterogenous and each workers’ ability is both private information and fixed for all time. Learning therefore occurs within employment relationships. The inferences, however, are confounded by moral hazard: the distribution of output is determined by both the worker’s type and by his u...

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. ...