Calculate a 95% confidence interval for estimator r(2) and determine if the estimator is statistically different from 0. You may assume that the Central Limit Theorem applies and therefore the estimator has an asymptotic Gaussian distribu- tion and you may use Bartlett's formula for the evaluation of the finite sample standard error. Finally state the outcome of your findings. Remark on the effect of such a small sample size on this conclusion.
Calculate a 95% confidence interval for estimator r(2) and determine if the estimator is statistically different from 0. You may assume that the Central Limit Theorem applies and therefore the estimator has an asymptotic Gaussian distribu- tion and you may use Bartlett's formula for the evaluation of the finite sample standard error. Finally state the outcome of your findings. Remark on the effect of such a small sample size on this conclusion.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:In this question you will consider properties of sample estimators
in time series context and perform inference and testing. You
may use R to assist you in calculations, but you should state the
formula of any estimator you are calculating as well as the value
you obtain for the estimator, and if R is used, the R command
utilised. You can either obtain the solution by calculator or by R.
You are given T 15 samples from a time series:
{y₁,..., y15}
{-0.92, 1.08, 0.20, -0.63, 1.36, 1.33, -1.06,
0.11, -1.00, −0.68, 1.81, 1.71, 0.93, −0.75, −2.16}
(c) Calculate a 95% confidence interval for estimator r(2) and
determine if the estimator is statistically different from 0.
You may assume that the Central Limit Theorem applies and
therefore the estimator has an asymptotic Gaussian distribu-
tion and you may use Bartlett's formula for the evaluation of
the finite sample standard error. Finally state the outcome
of your findings. Remark on the effect of such a small sample
size on this conclusion.
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