Comments on two side issues:
1. Yes, it's very pedantic, but clearly the
"C" in AIC stands for "criterion". Output
should thus be labelled in terms of "AI
criterion" or "AIC". No doubt you can find plenty
of uses of "AIC criterion", but that doesn't make
them good labelling. Note also that "criteria" is
the plural of "criterion" and not a singular
or an alternative spelling.
2. The code
syntax varlist(max=1)
tokenize `varlist'
foreach var of local varlist {
...
}
can be slimmed down. As there is only one
variable, you can just go
syntax varname
although the name in question will
be returned in `varlist'.
The statement
tokenize `varlist'
does no harm, but is just incantation here
as its results are never used.
Finally, the -foreach- loop is over a single
name, and thus again does no harm, but is
nevertheless unnecessary. Either you can
just define
local var `varlist'
or you can substitute `varlist' for `var'
wherever it occurs. These are points in
Rodrigo's original version: I guess Scott was
concentrating on getting it working, not
pedantry or polish.
Nick
[email protected]
[email protected]
> Here is one way and an example of the output.
> . webuse lutkepohl,clear
> (Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993
> Table E.1)
>
> . adftest dlinv, lag(10)
>
> Optimal lag by AIC criteria = 3
>
> Augmented Dickey-Fuller test for unit root Number of obs
> = 87
>
> ---------- Interpolated
> Dickey-Fuller --
> -------
> Test 1% Critical 5% Critical 10%
> Critical
> Statistic Value Value
>
> Value
> --------------------------------------------------------------
> ---------
> -------
> Z(t) -3.290 -3.528 -
> 2.900 -2.585
> --------------------------------------------------------------
> ---------
> -------
> MacKinnon approximate p-value for Z(t) = 0.0153
>
> . adftest dlinv, lag(10) bic
>
> Optimal lag by BIC criteria = 1
>
> Augmented Dickey-Fuller test for unit root Number of obs
> = 89
>
> ---------- Interpolated
> Dickey-Fuller --
> -------
> Test 1% Critical 5% Critical 10%
> Critical
> Statistic Value Value
>
> Value
> --------------------------------------------------------------
> ---------
> -------
> Z(t) -7.510 -3.525 -
> 2.899 -2.584
> --------------------------------------------------------------
> ---------
> -------
> MacKinnon approximate p-value for Z(t) = 0.0000
>
>
> . estimates stat
>
> --------------------------------------------------------------
> ---------
> -------
> Model | Obs ll(null) ll(model) df
> AIC BIC
> -------------+------------------------------------------------
> ---------
> -------
> adf_1_dlin~t | 89 112.0825 151.2462 3 -296.4923 -
> 289.0264
> adf_2_dlin~t | 88 110.3282 149.7886 4 -291.5772 -
> 281.6679
> adf_3_dlin~t | 87 108.9272 154.1228 5 -298.2457 -
> 285.9162
> adf_4_dlin~t | 86 109.1804 152.3199 6 -292.6398 -
> 277.9137
> adf_5_dlin~t | 85 107.9103 150.0973 7 -286.1947 -
> 269.0961
> adf_6_dlin~t | 84 106.1527 147.9145 8 -279.829 -
> 260.3825
> adf_7_dlin~t | 83 104.5957 147.4534 9 -276.9067 -
> 255.1372
> adf_8_dlin~t | 82 103.5983 146.9816 10 -273.9633 -
> 249.8961
> adf_9_dlin~t | 81 101.9356 144.9244 11 -267.8488 -
> 241.5098
> adf_10_dli~t | 80 100.188 142.758 12 -261.5159 -
> 232.9316
> --------------------------------------------------------------
> ---------
> -------
>
>
>
>
> ---------------------cut here ---------------------------
> program define adftest, rclass
> version 9.2
> syntax varlist(max=1) [if] [in] [, Lags(int-1) bic ]
> estimates clear
> tokenize `varlist'
> foreach var of local varlist{
> forvalues i=1/`lags'{
> //display "ADF(`i') of `var'"
> qui dfuller `var', constant lags(`i')
> local list storage(`i')
> estimates store adf_`i'_`var'
> }
> qui estimates stats `storage(`i')'
> matrix s=r(S)
> matrix criteria=J(`lags',2,.)
> forvalues i=1/`lags' {
> matrix criteria[`i',1]=s[`i',5]
> matrix criteria[`i',2]=s[`i',6]
> }
> if "`bic'" != "" {
> matrix criteria = criteria[1...,2]
> local crit "BIC"
> }
> else {
> matrix criteria = criteria[1...,1]
> local crit "AIC"
> }
> }
>
> mata: min_row(st_matrix("criteria"))
> local min = min[1,1]
> display " "
> display in gr "Optimal lag by `crit' criteria = " `min'
> dfuller `varlist', lag(`min') constant
>
> end
>
> mata:
> matrix function min_row(matrix A)
> {
> B = colmin(A)
> C = B:==A
> A2 = (0, 0)
> maxindex(C, 1, C, A2)
> st_matrix("min",C)
>
> }
> end
> ---------------------cut here ---------------------------
>
>
> ----- Original Message -----
> From: Rodrigo Martell <[email protected]>
> Date: Tuesday, August 8, 2006 10:45 pm
> Subject: st: Select minimum AIC from a matrix
> To: [email protected]
>
> > This might seem like a silly question but it stems from my
> > inexperience with Stata. I've created (with a bit of help from
> > StataCorp) a program that runs an ADF test for a series for a
> > number of lags specified by the user. I get to a point where the
> > AIC and BIC are reported in a matrix called "criteria". Here's the
> > code I've written:
> > ************************************************
> > program define adftest263, rclass
> > version 9
> > syntax varlist(max=1) [if] [in] [, /*
> > */Lags(int-1)]
> > tokenize `varlist'
> > foreach var of local varlist{
> > forvalues i=1/`lags'{
> > display "ADF(`i') of `var'"
> > dfuller `var', constant lags(`i') regress
> > local list storage(`i')
> > estimates store adf_`i'_`var'
> > }
> > estimates stats `storage(`i')'
> > matrix s=r(S)
> > matrix list s
> > matrix criteria=J(`lags',2,.)
> > forvalues i=1/`lags' {
> > matrix criteria[`i',1]=s[`i',5]
> > matrix criteria[`i',2]=s[`i',6]
> > }
> > matrix list criteria
> > ***** Up to here it works fine *********
> > }
> > end
> > ***********************************************************
> > I want to now select the first column (I presume a command with
> > j=1 and any `i'?) of this matrix and calculate the minimum. This
> > minimum should
> > correspond to the "best fitting (by AIC) ADF lag length". I want
> > to select that lag length and run it for the series to report the
> > result.Does anyone know how to do this? I'd greatly appreciate a
> > hand. An example of what I'm trying to do:
> >
> > The result of running the program with an ADF(2) would be a matrix
> > displayed like this. The first column corresponds to the AIC and
> > the second to the BIC, while the rows correspond to the number of
> > lags in the ADF:
> >
> > c1 c2
> > r1 1.5 1.6
> >
> > r2 0.5 0.5
> >
> > From the output, I want to calculate the minimum of column c1
> > (minimum AIC), which would be 0.5, which corresponds to the second
> > row, which implies an ADF(2) is the best fitting model. I would
> > then want to run -dfuller [series], constant lags(2)-
> >
> > I would love a hand if anyone can give me one. Thanks!
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