Local Power of Tests of Fit for Normality of Autoregression

03/07/2020
by   Michael Boldin, et al.
0

We consider a stationary AR(p) model. The autoregression parameters are unknown as well as the distribution of innovations. Based on the residuals from the parameter estimates, an analog of empirical distribution function is defined and the tests of Kolmogorov's and ω^2 type is constructed for testing hypotheses on the normality of innovations. We obtain the asymptotic power of these tests under local alternatives.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/29/2020

On the Power of Symmetrized Pearson's Type Test under Local Alternatives in Autoregression with Outliers

We consider a stationary linear AR(p) model with observations subject to...
research
01/08/2020

Domination Number of an Interval Catch Digraph Family and its use for Testing Uniformity

We consider a special type of interval catch digraph (ICD) family for on...
research
06/14/2021

Bahadur efficiency of EDF based normality tests when parameters are estimated

In this paper some well-known tests based on empirical distribution func...
research
09/09/2022

On the Asymptotic Properties of a Certain Class of Goodness-of-Fit Tests Associated with Multinomial Distributions

The object of study is the problem of testing for uniformity of the mult...
research
10/10/2019

Robust Likelihood Ratio Tests for Incomplete Economic Models

This study develops a framework for testing hypotheses on structural par...
research
11/16/2020

New characterization based exponentiality tests for randomly censored data

Recently, the characterization based approach for the construction of go...
research
04/05/2019

Aggregated kernel based tests for signal detection in a regression model

Considering a regression model, we address the question of testing the n...

Please sign up or login with your details

Forgot password? Click here to reset