function value = eco561arma(b, y, p, q);
%Define a separate matlab function to do minimization
mu = b(1);
%Extract mu
phi = b(2:1+p);
%Extract phi
arroots = roots([1; -phi]);
%Calculate the inverted
(1) Phi1=0.2729 Phi2=0.2367 Phi3= 0.3916.
(2) According to the plot for residuals we cannot observe some pattern from it.
So the residual series et likely to be generated from a white noise process.
(
function partial = eco561partial(rho);
%Define a separate matlab function to calculate partial autocorrelations from autocorrelations
rhomatrix = zeros(length(rho), length(rho);
%Allocate a matrix to
function value = eco561ma(b, y, q);
%Define a separate matlab function to do minimization
mu = b(1);
%Extract mu
theta = b(2:end);
%Extract theta
maroots = roots([1; theta]);
%Calculate the inverted
function eco561chpt7
%Define a matlab function
%Chapter 7, realizations of two MA(1) processes
epsilon = randn(151, 1);
%Simulate draws from a Gaussian white noise process
y1 = epsilon(2:end)+0.4*epsi
function eco561chpt6
%Define a matlab function
%Chapter 6, realization of white noise process
epsilon = randn(150, 1);
%Simulate draws from a Gaussian white noise process
y = epsilon;
%Construct the s
function eco561chpt5
%Define a matlab function
%Chapter 5, liquor sales
data = load('LIQUOR.DAT', '-ascii');
%Read the data set called `LIQUOR.DAT' in ascii form.
figure;
%Create the figure.
plot(80+1
function [rho, qlb, partial] = eco561auto(series, tau);
%Define a separate matlab function to calculate sample autocorrelations, Ljung-box Q-statistic, and sample partial autocorrelations
rho = zeros(
1. The assumption is not reasonable. Since from residual plot, we still
can observe some pattern.
2. Yes, we can use sample autocorrelation and sample partial
autocorrelation to see the distribution:
1.
2.
If T goes to infinity, there will be no differences between the sample autocorrelations
and partial autocorrelations plotted in question 2 and their population counterparts
shown in question 1.