I wish to simulate the following stochastic process, for i = 1...N
individuals and t=1...T periods:
y_{i,t} = y_0 + lambda Ey_{t-1} + epsilon_{i,t}
where Ey_{t-1} is the average of y over the N individuals computed at time
t-1.
My solution (below) works but is incredibly slow. Is there a faster
Matteo,
I tried your example code using R 3.1.1 on an iMac (24-inch, Early 2009), 3.06
GHz Intel Core 2 Duo, 8 GB 1333 MHz DDR3, NVIDIA GeForce GT 130 512 MB
running Mac OS X 10.10 (Yosemite).
After entering your code, the elapsed time from the time I hit return to
when the graphics appeared was
Matteo,
Ah — OK, N=20, I did not catch that. You have nested for loops, which R is
known to be exceedingly slow at handling — if you can reorganize the code
to eliminate the loops, your performance will increase significantly.
Tom
On Thu, Nov 6, 2014 at 7:47 AM, Matteo Richiardi
I find that representing the simulated data as a T row by N column matrix
allows for a clearer and faster simulation function. E.g., compare the
output of the following two functions, the first of which uses your code
and the second a matrix representation (which I convert to a data.frame at
the
Loops are not slow, but your code did a lot of unneeded operations in each
loop.
E.g, you computed
D$id==i D$t==t
for each row of D. That involves 2*nrow(D) equality tests for each of the
nrow(D)
rows, i.e., it is quadratic in N*T.
Then you did a data.frame replacement operation
D[k,]$y
SNIP
On Thu, Nov 6, 2014 at 2:05 PM, Matteo Richiardi matteo.richia...@gmail.com
wrote:
SNIP
Final question: in your code you have mean(M[t-1L,]): what is the 'L'
for? I removed it at apparently the code produces the same output...
SNIP
The constant 1L is stored as an integer; the
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