Improved Tests for the First-Order Autoregressive Model With Heteroscedasticity

1995 ◽  
Vol 52 (1) ◽  
pp. 71-83
Author(s):  
John. Lyon ◽  
Chin-Ling. Tsai
Keyword(s):  
Autoregressive Model ◽  
First Order

PATH INTEGRAL METHOD FOR LIMITING DISTRIBUTION OF AN ESTIMATOR ARISING FROM AN AR(1)-PROCESS WITH A UNIT ROOT

2013 ◽  
Vol 16 (04) ◽  
pp. 1350029
Author(s):  
SHIH-FENG HUANG ◽  
YUH-JIA LEE ◽  
HSIN-HUNG SHIH
Keyword(s):  
Characteristic Function ◽  
Path Integral ◽  
Unit Root ◽  
Autoregressive Model ◽  
Integral Method ◽  
Unit Root Test ◽  
Random Variable ◽  
Limiting Distribution ◽  
First Order ◽  
Path Integral Method

We propose the path-integral technique to derive the characteristic function of the limiting distribution of the unit root test in a first order autoregressive model. Our results provide a new and useful approach to obtain the closed form of the characteristic function of a random variable associated with the limiting distribution, which is realized as a ratio of Brownian functionals on the classical Wiener space.

A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model

1982 ◽  
Vol 14 (8) ◽  
pp. 1023-1030 ◽  
Author(s):  
L Anselin
Keyword(s):  
Monte Carlo Simulation ◽  
Autoregressive Model ◽  
Ad Hoc ◽  
Simulation Experiment ◽  
Small Sample ◽  
Bayesian Estimator ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Small Sample Properties ◽  
Spatial Autoregressive

This note considers a Bayesian estimator and an ad hoc procedure for the parameters of a first-order spatial autoregressive model. The approaches are derived, and their small sample properties compared by means of a Monte Carlo simulation experiment.

A Robust Model for Estimating and Testing for Means in Single Subject Experiments

1978 ◽  
Vol 20 (6) ◽  
pp. 717-724 ◽  
Author(s):  
James J. Higgins
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Autoregressive Model ◽  
Error Term ◽  
Behavioral Research ◽  
Single Subject ◽  
First Order ◽  
Normal Approximations ◽  
Robust Model ◽  
Tests Of Hypotheses

The first order autoregressive model is proposed as a robust model for estimating and testing for means in single subject experiments. It has the advantage of mathematical simplicity, and it provides good approximations to a number of other models of the type typically encountered in behavioral research. Practical considerations on the use of the model are considered including: tests of hypotheses and confidence intervals, sample size requirements, normal approximations, and advantages of the model over the independent error term model. Inferences for means and differences of means are considered.

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