LEAST SQUARES Estimation code

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dav
dav on 7 Feb 2013
Commented: yem on 16 Dec 2014
Anybody know the code to estimate an ARMA model using LEAST SQUARES?
Thanks.

Accepted Answer

Shashank Prasanna
Shashank Prasanna on 8 Feb 2013
Edited: Shashank Prasanna on 8 Feb 2013
To show you an example I am going to generate some data from the following ARMA model. I am generating this using the ARIMA function in the econometrics toolbox. If you don't have it don't worry because I am using this data to demonstrate how to estimate the coefficients using least squares. How ever I would like to inform you that that a more popular approach is to use MLE.
Generate some data to simulate.
Here AR lag 1, coeff 0.3
MA lag 1, coeff 0.2
rng(5);
simModel = arima('AR',0.3,'MA',0.2,'Constant',0,'Variance',1);
% simModel = arima('AR',0.4,'Constant',0,'Variance',1);
Y = simulate(simModel,500);
The following code will estimate the coefficients using least squares using MATLAB's \ operator.
>> rng(5);
inn=randn(500,1);
[Y -[0;Y(1:end-1)] -[0;inn(1:end-1)]]\inn
ans =
1.0000
0.3000
0.2000
The first number is for y(t) which is 1. the AR is 0.3 and MA is 0.2.
  3 Comments
Shashank Prasanna
Shashank Prasanna on 8 Feb 2013
RNG is used to set the random number generator seed, it could be anything, i just used 5. Notice that I reuse it again while I estimate so that I use the exact same random numbers during estimation that I used during data generation.
inn is commonly known as innovations or error terms, and it is assumed to be white noise or normally distributed with 0 mean 1 var, but could be something else. i use the randn function to generate some inn. The equation I used is just the solution to a linear system Ax=b which can be solved in MATLAB for x by performing A\b.
Lastly, MLE is indeed a popular mathod and MATLAB is capable to solving MLEs as long as you formulate you problem that way. search documentation for MLE
hth
dav
dav on 16 Feb 2013
Thanks alot. what I really have to do is as follows. I used the following R code to estimate ARMA model. Note that first, I have generated a garch data set. Now, according to a theory I know epsi^2 has an ARMA model. So when when I estimate epsi^2 using LEAST SQUARES, I should get parameter estimates close to the same parameter values of the GARCH model. I have to have an additional condition that constraint that parameter estimates are greater than zero. I am actually trying to write a code to do step 1 of the paper titled " computationally efficient bootstrap prediction intervals for arch and garch processes " by Bei Cheng.
If you could help me with this, it is greatly appreciated.
R CODE: a0 = 0.05; a1 = 0.1; b1 = 0.85
nu = rnorm(2300)
epsi = rep(0, 2300)
h = rep(0, 2300)
for (i in 2: 2300) { h[i] = a0 + a1 * epsi[i-1]^2 + b1 * h[i-1] ; epsi[i] = nu[i] * sqrt(h[i])}
epsi = epsi[2001:2300]
epsi=epsi*epsi
arma(epsi,order=c(1,1))
(my problem with using R is that it doent have enough memory to do the entire process)

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More Answers (1)

Azzi Abdelmalek
Azzi Abdelmalek on 7 Feb 2013
  4 Comments
Azzi Abdelmalek
Azzi Abdelmalek on 7 Feb 2013
Edited: Azzi Abdelmalek on 7 Feb 2013
% n: is the order of your system
% u: input signal
% y : output signal
k1=5 % k1 >n
k2=30
teta=least_square(u,y,n,k1,k2)
yem
yem on 16 Dec 2014
hi i need to identify systems with 2 delays with least square algorithm anyhelp please

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