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Time Series in Matlab 2008
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Time Series in Matlab 1
14.384 Time Series Analysis, Fall 2007
Recitation by Paul Schrimpf
Supplementary to lectures given by Anna Mikusheva
September 11, 2008
Recitation 2: Time Series in Matlab
Time Series in Matlab
In problem set 1, you need to estimate spectral densities and apply common filters. You
can use any software you would like, but we recommend
using Matlab. It may be easier
to do simple things using more statistics oriented programs like
Stata or RATs, since these
programs include pre-packaged commands for man
y common tasks, but you will learn more
by writing the co
de yourself. Also, it is generally easier to write programs for new estimators
in a full-featured programming language like Matlab’s than in
the language of statistics
oriented programs. Finally,
I recommend using Matlab because I happen to use Matlab, and
I will be more likely to
be able to provide help if you need it.
These notes cover
some slightly obscure Matlab commands that can be useful for time
series. For a more general
overview, see
http://web.mit.edu/~paul_s/www/14.170/matlab.html
Disclaimer: I wrote
these
notes last year, and I am not entirely sure that they are com-
pletely correct. Many of the
commands covered are from Matlab’s signal processing toolbox,
and they have different names and may
do slightly different things than what an econometri-
cian would expect. Always read Matlab’s help and
documentation before using a command.
When in doubt, double check that the c
ommand does what you think.
Simulating an ARMA Model
a(L)y
t
= b(L)e
t
1 clear;
2 a = [1 0.5]; % AR coeffs
3 b = [1 0.4 0.3]; % MA coeffs
4 T = 1000;
5 e = randn(T,1); % generate gaussian white noise
6 y = filter(b,a,e); % generate y
The filter function can be used to generate data from an ARMA model, or apply a filter
to a series.
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