Testing for Unit Roots in a Nearly Nonstationary Spatial Autoregressive Process

2000 ◽  
Vol 52 (1) ◽  
pp. 71-83 ◽  
Author(s):  
B. B. Bhattacharyya ◽  
X. Li ◽  
M. Pensky ◽  
G. D. Richardson
Keyword(s):  
Unit Roots ◽  
Autoregressive Process ◽  
Spatial Autoregressive

scholarly journals A Note on the Properties of Generalised Separable Spatial Autoregressive Process

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Mahendran Shitan ◽  
Shelton Peiris
Keyword(s):  
Three Dimensional ◽  
Real Data ◽  
Autoregressive Process ◽  
Spatial Modelling ◽  
Spectral Density Function ◽  
Data Sets ◽  
Additional Parameter ◽  
New Class ◽  
Spatial Autoregressive ◽  
Modelling And Forecasting

Spatial modelling has its applications in many fields like geology, agriculture, meteorology, geography, and so forth. In time series a class of models known as Generalised Autoregressive (GAR) has been introduced by Peiris (2003) that includes an index parameterδ. It has been shown that the inclusion of this additional parameter aids in modelling and forecasting many real data sets. This paper studies the properties of a new class of spatial autoregressive process of order 1 with an index. We will call this aGeneralised Separable Spatial Autoregressive(GENSSAR) Model. The spectral density function (SDF), the autocovariance function (ACVF), and the autocorrelation function (ACF) are derived. The theoretical ACF and SDF plots are presented as three-dimensional figures.

On the First-Order Autoregressive Process with Infinite Variance

1989 ◽  
Vol 5 (3) ◽  
pp. 354-362 ◽  
Author(s):  
Ngai Hang Chan ◽  
Lanh Tat Tran
Keyword(s):  
Strong Consistency ◽  
Domain Of Attraction ◽  
Unit Roots ◽  
Autoregressive Process ◽  
Ordinary Least Squares ◽  
Seasonal Difference ◽  
Infinite Variance ◽  
Stable Law ◽  
First Order ◽  
Heavy Tailed

For a first-order autoregressive process Yt = βYt−1 + ∈t where the ∈t'S are i.i.d. and belong to the domain of attraction of a stable law, the strong consistency of the ordinary least-squares estimator bn of β is obtained for β = 1, and the limiting distribution of bn is established as a functional of a Lévy process. Generalizations to seasonal difference models are also considered. These results are useful in testing for the presence of unit roots when the ∈t'S are heavy-tailed.

scholarly journals METODE SPATIAL AUTOREGRESSIVE DALAM MEMODELKAN MASYARAKAT YANG BERPERILAKU MCK DI SUNGAI

2017 ◽  
Vol 6 (4) ◽  
pp. 233
Author(s):  
KOMANG KOKOM SUCAHYATI DEWI P ◽  
MADE SUSILAWATI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Autoregressive Process ◽  
Spatial Effect ◽  
Spatial Autoregressive

Spatial autoregressive (SAR) is a model of spatial regression which assumes that autoregressive process is only for the dependent variable by considering the spatial effect. The spatial effect consists of spatial dependence and spatial heterogeneity. One of problems which considers spatial effect is the case of society which do public bathing, washing, and toilets facilities (PBWTF) in the river, in Blahbatuh District. The aim of this research is to obtaine the model of society which still do PBWTF in the river, in Blahbatuh Districts in 2016 and to determine the factors that influence it. In this research, we obtained the model which is able to illustrate the case of society which do PBWTF and the factors that influence it such as the amount of householde which do not have latrine and the amount of family who lives near the river in every subvillage in Blahbatuh District.   Keywords: SAR, Spatial Effect, and Public Bathing, Washing, and Toilets Facilities in the River.  

scholarly journals PEMODELAN PENYEBARAN KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA DENPASAR DENGAN METODE SPATIAL AUTOREGRESSIVE (SAR)

2017 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
NI MADE SURYA JAYANTI ◽  
I WAYAN SUMARJAYA ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Model ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Hemorrhagic Fever ◽  
Autoregressive Process ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Regression Model ◽  
Spatial Autoregressive

One of spatial regression model is Spatial Autoregressive (SAR), which assumes that the autoregressive process only on the dependent variable only by considering the spatial effects. There are two aspects of spatial effects, that is spatial dependence and spatial heterogeneity. One of the problems which considers spatial effect is the spread of Dengue Hemorrhagic Fever (DHF). Denpasar City is an endemic DHF disease because there have been DHF cases in three consecutive years or more. The purpose of this research is to estimate the spread of DHF in  Denpasar City along with the factors that affect it. The results show that the factors that influence the spread of DHF are neighborhood, area and the role of Jumantik at the every village in Denpasar City.

On the generation and origin of I/f noise

1999 ◽  
Vol 4 ◽  
pp. 87-96 ◽  
Author(s):  
B. Kaulakys ◽  
T. Meškauskas
Keyword(s):  
Point Process ◽  
Solvable Model ◽  
Autoregressive Process ◽  
Wide Range ◽  
Occurrence Times

Simple analytically solvable model exhibiting 1/f spectrum in any desirably wide range of frequency is analysed. The model consists of pulses (point process) whose interevent times obey an autoregressive process with small damping. Analysis and generalizations of the model indicate to the possible origin of 1/f noise, i.e. random increments between the occurrence times of particles or pulses resulting in the clustering of the pulses.

Sign in / Sign up

Export Citation Format

Share Document