# \begin{code}
arma p q ; y
# \end{code}
# \begin{code}
arma p q ; y --conditional
# \end{code}
# \begin{code}
arma p q ; y const x1 x2
# \end{code}
# \begin{code}
arma p q ; y const x1 x2 --conditional
# \end{code}
# \begin{code}
arma p q ; P Q ; y
# \end{code}
# \begin{code}
arma 1 1 ; 1 1 ; y
# \end{code}
# \begin{code}
matrix pvec = {1, 4}
arma pvec 1 ; y
arma {1 4} 1 ; y
# \end{code}
# \begin{code}
arma p d q ; y
# \end{code}
# \begin{code}
series tmp = y
loop for i=1..d
tmp = diff(tmp)
end loop
arma p q ; tmp
# \end{code}
# \begin{code}
arma p d q ; P D Q ; y
# \end{code}
# \begin{code}
arma 1 0 0 ; 1 1 1 ; y
# \end{code}
# \begin{code}
genr dsy = sdiff(y)
arma 1 0 ; 1 1 ; dsy
# \end{code}
# \begin{code}
open data10-1
arma 1 1 ; r
arma 1 1 ; r --conditional
# \end{code}
# \begin{code}
matrix start = { 0, 0.85, 0.34 }
set initvals start
arma 1 1 ; y
# \end{code}
# \begin{code}
arma p q ; y --x-12-arima
# \end{code}
# \begin{code}
y fc1 fc2 fcdiff
1981:1 7.964086 7.940930 0.02668 0.02668
1981:2 7.978654 7.997576 0.03349 0.03349
1981:3 8.009463 7.997503 0.01885 0.01885
1981:4 8.015625 8.033695 0.02423 0.02423
1982:1 8.014997 8.029698 0.01407 0.01407
1982:2 8.026562 8.046037 0.01634 0.01634
1982:3 8.032717 8.063636 0.01760 0.01760
1982:4 8.042249 8.081935 0.01830 0.01830
1983:1 8.062685 8.100623 0.01869 0.01869
1983:2 8.091627 8.119528 0.01891 0.01891
1983:3 8.115700 8.138554 0.01903 0.01903
1983:4 8.140811 8.157646 0.01909 0.01909
# \end{code}
# \begin{code}
adf 4 x1 --c --ct
# \end{code}
# \begin{code}
kpss m y
# \end{code}
# \begin{code}
kpss n y --trend
# \end{code}
# \begin{code}
garch p q ; y const x
# \end{code}
# \begin{code}
loop control-expression [ --progressive | --verbose | --quiet ]
loop body
endloop
# \end{code}
# \begin{code}
loop while essdiff > .00001
# \end{code}
# \begin{code}
open greene22_2
open greene22_2
discrete Z8
v8 = values(Z8)
n = rows(v8)
n = rows(v8)
loop for i=1..n
scalar xi = v8[$i]
smpl (Z8=xi) --restrict --replace
printf "mean(Y | Z8 = %g) = %8.5f, sd(Y | Z8 = %g) = %g\n", \
xi, mean(Y), xi, sd(Y)
end loop
# \end{code}
# \begin{code}
loop foreach i peach pear plum
print "$i"
endloop
# \end{code}
# \begin{code}
genr time
loop foreach i AL..WY
ols $i const time
endloop
# \end{code}
# \begin{code}
list ylist = y1 y2 y3
loop foreach i ylist
ols $i const x1 x2
endloop
# \end{code}
# \begin{code}
loop for (r=0.01; r<.991; r+=.01)
# \end{code}
# \begin{code}
Model1 <- ols Ct 0 Yt
# \end{code}
# \begin{code}
"Model 1" <- ols Ct 0 Yt
# \end{code}
# \begin{code}
Model1.show
# \end{code}
# \begin{code}
"Model 1".show
# \end{code}
# \begin{code}
CrossPlot <- gnuplot Ct Yt
CrossPlot.show
# \end{code}
# \begin{code}
ADF1 <- adf 2 x1
ADF1.show
# \end{code}
# \begin{code}
setobs unitvar timevar --panel-vars
# \end{code}
# \begin{code}
setobs 20 1:1 --stacked-time-series
setobs 5 1:1 --stacked-cross-section
# \end{code}
# \begin{code}
open panel.txt
genr x1 = stack(p1..p5) --length=50
genr x2 = stack(p1..p5) --offset=50 --length=50
setobs 50 1:1 --stacked-cross-section
store panel.gdt x1 x2
# \end{code}
# \begin{code}
genr x = stack(p1,p3,p5)
# \end{code}
# \begin{code}
open panel.txt
loop for i=1..20
genr k = ($i - 1) * 50
genr x$i = stack(p1..p5) --offset=k --length=50
endloop
setobs 50 1.01 --stacked-cross-section
store panel.gdt x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 \
x11 x12 x13 x14 x15 x16 x17 x18 x19 x20
# \end{code}
# \begin{code}
genr time
genr year = 1960 + (5 * time)
genr markers = "%s:%d", marker, year
# \end{code}
# \begin{code}
# Gretl: various illustrative datafiles
"arma","artificial data for ARMA script example"
"ects_nls","Nonlinear least squares example"
"hamilton","Prices and exchange rate, U.S. and Italy"
# \end{code}
# \begin{code}
# Gretl: various sample scripts
"arma","ARMA modeling","artificial data"
"ects_nls","Nonlinear least squares (Davidson)","artificial data"
"leverage","Influential observations","artificial data"
"longley","Multicollinearity","US employment"
# \end{code}
# \begin{code}
foo = x1 * x2
# \end{code}
# \begin{code}
function function-name(parameters)
function body
end function
# \end{code}
# \begin{code}
function myfunc(series y, list xvars, bool verbose)
# \end{code}
# \begin{code}
function myfunc(series *y, scalar *b)
# \end{code}
# \begin{code}
int order[1:12:4]
# \end{code}
# \begin{code}
bool verbose[0]
# \end{code}
# \begin{code}
function myfunc2(void)
# \end{code}
# \begin{code}
# function definition
function ols_ess(series y, list xvars)
ols y 0 xvars --quiet
scalar myess = $ess
printf "ESS = %g\n", myess
return scalar myess
end function
# main script
open data4-1
list xlist = 2 3 4
# function call (the return value is ignored here)
ols_ess(price, xlist)
# \end{code}
# \begin{code}
# function definition
function get_uhat(series y, list xvars)
ols y 0 xvars --quiet
series uh = $uhat
return series uh
end function
# main script
open data4-1
list xlist = 2 3 4
# function call
series resid = get_uhat(price, xlist)
# \end{code}
# \begin{code}
function myfunc delete
function get_uhat clear
# \end{code}
# \begin{code}
function triple1(series x)
series ret = 3*x
return series ret
end function
function triple2(series *x)
series ret = 3*x
return series ret
end function
# \end{code}
# \begin{code}
function get_uhat_and_ess(series y, list xvars, scalar *ess)
ols y 0 xvars --quiet
ess = $ess
series uh = $uhat
return series uh
end function
# main script
open data4-1
list xlist = 2 3 4
# function call
scalar SSR
series resid = get_uhat_and_ess(price, xlist, &SSR)
# \end{code}
# \begin{code}
function get_uhat_and_ess(series y, list xvars, scalar *ess[null])
ols y 0 xvars --quiet
if !isnull(ess)
ess = $ess
endif
series uh = $uhat
return series uh
end function
# \end{code}
# \begin{code}
series resid = get_uhat_and_ess(price, xlist, null)
# \end{code}
# \begin{code}
Elapsed time:
without pointers (copy) = 3.66 seconds,
with pointers (no copy) = 0.01 seconds.
# \end{code}
# \begin{code}
loop foreach i X
scalar sd_$i = sd($i)
end loop
# \end{code}
# \begin{code}
function myfunc (scalar y, const list X)
# \end{code}
# \begin{code}
function myfunc (scalar y, list X[null])
# \end{code}
# \begin{code}
function movavg (series y, string vname)
series @vname_2 = (y+y(-1)) / 2
series @vname_4 = (y+y(-1)+y(-2)+y(-3)) / 4
list retlist = @vname_2 @vname_4
return list retlist
end function
open data9-9
list malist = movavg(nocars, "nocars")
print malist --byobs
# \end{code}
# \begin{code}
function somefun (series y)
# \end{code}
# \begin{code}
function namefun (series y)
printf "the series given as 'y' was named %s\n", argname(y)
end function
open data9-7
namefun(QNC)
# \end{code}
# \begin{code}
the series given as 'y' was named QNC
# \end{code}
# \begin{code}
return scalar SSR
# \end{code}
# \begin{code}
function make_cubes (list xlist)
list cubes = null
loop foreach i xlist --quiet
series $i3 = $i^3
setinfo $i3 -d "cube of $i"
list cubes += $i3
end loop
return list cubes
end function
open data4-1
list xlist = price sqft
list cubelist = make_cubes(xlist)
print xlist cubelist --byobs
labels
# \end{code}
# \begin{code}
if nelem(xlist) = 0
funcerr "xlist must not be empty"
end if
# \end{code}
# \begin{code}
set echo on
set messages on
# \end{code}
# \begin{code}
function pc(series y)
series foo = diff(y)/y(-1)
return series foo
end function
# \end{code}
# \begin{code}
genr x = uniform()
genr dpcx = pc(x)
print x dpcx --byobs
# \end{code}
# \begin{code}
include pc.gfn
open np
foo = pc(iprod)
# \end{code}
# \begin{code}
gretlcli -b scriptfile > outputfile
# \end{code}
# \begin{code}
(*
DATA9-6:
Data on log(money), log(income) and interest rate from US.
Source: Stock and Watson (1993) Econometrica
(unsmoothed data) Period is 1900-1989 (annual data).
Data compiled by Graham Elliott.
*)
lmoney lincome intrate ;
1 1900 1989 BYOBS
# \end{code}
# \begin{code}
G0M910 Composite index of 11 leading indicators (1987=100)
M 1948.01 - 1995.11 n = 575
currbal Balance of Payments: Balance on Current Account; SA
Q 1960.1 - 1999.4 n = 160
# \end{code}
# \begin{code}
# Federal Reserve Board (interest rates)
# \end{code}
# \begin{code}
apt-get install gcc autoconf automake1.9 libtool flex bison gcc-doc \
libc6-dev libc-dev libgfortran1 libgfortran1-dev gettext pkgconfig
# \end{code}
# \begin{code}
cvs -d:pserver:anonymous@gretl.cvs.sourceforge.net:/cvsroot/gretl login
cvs -z3 -d:pserver:anonymous@gretl.cvs.sourceforge.net:/cvsroot/gretl co -P gretl
# \end{code}
# \begin{code}
cvs update -d -P
# \end{code}
# \begin{code}
./configure --help
# \end{code}
# \begin{code}
./configure --prefix=/usr
# \end{code}
# \begin{code}
./configure --enable-build-doc
# \end{code}
# \begin{code}
make
# \end{code}
# \begin{code}
make install
# \end{code}
# \begin{code}
sudo make install
# \end{code}
# \begin{code}
list xlist = 1 2 3 4
list reglist = income price
# \end{code}
# \begin{code}
list xlist = x1 x2 x3 x4
ols y 0 xlist
# \end{code}
# \begin{code}
list xlist = 1 2 3
xlist = 4 5 6
# \end{code}
# \begin{code}
list xlist = xlist 5 6 7
xlist = 9 10 xlist 11 12
# \end{code}
# \begin{code}
xlist += cpi
# \end{code}
# \begin{code}
xlist -= cpi
# \end{code}
# \begin{code}
list L3 = L1 || L2
# \end{code}
# \begin{code}
list L3 = L1 && L2
# \end{code}
# \begin{code}
matrix m = {1,2,3,4}
list L = m
# \end{code}
# \begin{code}
series xl1 = log(x1)
series xl2 = log(x2)
list xlogs = xl1 xl2
genr is1 = islist(xlogs)
genr is2 = islist(xl1)
# \end{code}
# \begin{code}
list xlist = 1 2 3
nl = nelem(xlist)
# \end{code}
# \begin{code}
? list xlist = x1 x2 x3
Added list 'xlist'
? xlist
x1 x2 x3
# \end{code}
# \begin{code}
list xlist = x1 x2 x3
list lxlist = log(xlist)
list difflist = diff(xlist)
# \end{code}
# \begin{code}
list xlist = x1 x2 x3
list laglist = lags(2, xlist)
# \end{code}
# \begin{code}
scalar order = 4
list laglist = lags(order, xlist)
# \end{code}
# \begin{code}
list xlist = x1 x2 x3
series xok = ok(xlist)
# \end{code}
# \begin{code}
string s1 = "some stuff I want to save"
string s2 = getenv("HOME")
string s3 = s1 + 11
# \end{code}
# \begin{code}
string s1 = "some stuff I want to "
string s1 += "save"
# \end{code}
# \begin{code}
scalar x = 8
sprintf foo "var%d", x
# \end{code}
# \begin{code}
? scalar x = 8
scalar x = 8
Generated scalar x (ID 2) = 8
? sprintf foo "var%d", x
Saved string as 'foo'
? print "@foo"
var8
# \end{code}
# \begin{code}
? print @foo
# \end{code}
# \begin{code}
print var8
# \end{code}
# \begin{code}
string vstr = "variance"
Generated string vstr
printf "vstr: %12s\n", vstr
vstr: variance
# \end{code}
# \begin{code}
? string vstr_copy = vstr
# \end{code}
# \begin{code}
? string user = getenv("USER")
Saved string as 'user'
? string home = getenv("HOME")
Saved string as 'home'
? print "@user's home directory is @home"
cottrell's home directory is /home/cottrell
# \end{code}
# \begin{code}
? string temp = getenv("TEMP")
Saved empty string as 'temp'
? scalar x = strlen(temp)
Generated scalar x (ID 2) = 0
# \end{code}
# \begin{code}
? string hw = "hello world"
Saved string as 'hw'
? string w = strstr(hw, "o")
Saved string as 'w'
? print "@w"
o world
# \end{code}
# \begin{code}
coint2 p ylist [ ; xlist [ ; zlist ] ]
# \end{code}
# \begin{code}
vecm p r ylist [ ; xlist [ ; zlist ] ]
# \end{code}
# \begin{code}
open denmark
coint2 2 LRM LRY IBO IDE --rc --seasonal
# \end{code}
# \begin{code}
Johansen test:
Number of equations = 4
Lag order = 2
Estimation period: 1974:3 - 1987:3 (T = 53)
Case 2: Restricted constant
Rank Eigenvalue Trace test p-value Lmax test p-value
0 0.43317 49.144 [0.1284] 30.087 [0.0286]
1 0.17758 19.057 [0.7833] 10.362 [0.8017]
2 0.11279 8.6950 [0.7645] 6.3427 [0.7483]
3 0.043411 2.3522 [0.7088] 2.3522 [0.7076]
# \end{code}
# \begin{code}
open money.gdt
smpl 1954:1 1994:4
vecm 6 2 m infl cpr y tbr --rc
# \end{code}
# \begin{code}
Maximum likelihood estimates, observations 1954:1-1994:4 (T = 164)
Cointegration rank = 2
Case 2: Restricted constant
beta (cointegrating vectors, standard errors in parentheses)
m 1.0000 0.0000
(0.0000) (0.0000)
infl 0.0000 1.0000
(0.0000) (0.0000)
cpr 0.56108 -24.367
(0.10638) (4.2113)
y -0.40446 -0.91166
(0.10277) (4.0683)
tbr -0.54293 24.786
(0.10962) (4.3394)
const -3.7483 16.751
(0.78082) (30.909)
# \end{code}
# \begin{code}
restrict
b[1,1] = -1
b[1,3] = 0
b[2,1] = 0
b[2,3] = -1
end restrict
# \end{code}
# \begin{code}
Cointegrating vectors (standard errors in parentheses)
m -1.0000 0.0000
(0.0000) (0.0000)
infl -0.023026 0.041039
(0.0054666) (0.027790)
cpr 0.0000 -1.0000
(0.0000) (0.0000)
y 0.42545 -0.037414
(0.033718) (0.17140)
tbr -0.027790 1.0172
(0.0045445) (0.023102)
const 3.3625 0.68744
(0.25318) (1.2870)
# \end{code}
# \begin{code}
b[1,1] = 1.618
b[1,4] + 2*b[2,5] = 0
a[1,3] = 0
a[1,1] - a[1,2] = 0
# \end{code}
# \begin{code}
restrict
b1 + b2 = 0
end restrict
# \end{code}
# \begin{code}
restrict
a3 = 0
a4 = 0
end restrict
# \end{code}
# \begin{code}
matrix I4 = I(4)
matrix vR = I4**(I4~zeros(4,1))
matrix vq = mshape(I4,16,1)
restrict
R = vR
q = vq
end restrict
# \end{code}
# \begin{code}
open denmark.gdt
vecm 2 1 LRM LRY IBO IDE --rc --seasonals
matrix BA = {1, -1, 6, -6, -6, -0.2, 0.1, 0.02, 0.03}
set initvals BA
restrict
b[1] = 1
b[1] + b[2] = 0
b[3] + b[4] = 0
end restrict
# \end{code}
# \begin{code}
open denmark.gdt
vecm 2 1 LRM LRY IBO IDE --rc --seasonals
matrix phi = {-8, -6}
set initvals phi
restrict --lbfgs
b[1] = 1
b[1] + b[2] = 0
b[3] + b[4] = 0
end restrict
# \end{code}
# \begin{code}
restrict
b[1] = 1
b[1] + b[2] = 0
end restrict
# \end{code}
# \begin{code}
Model 1: Logit estimates using the 32 observations 1-32
Dependent variable: GRADE
VARIABLE COEFFICIENT STDERROR T STAT SLOPE
(at mean)
const -13.0213 4.93132 -2.641
GPA 2.82611 1.26294 2.238 0.533859
TUCE 0.0951577 0.141554 0.672 0.0179755
PSI 2.37869 1.06456 2.234 0.449339
Mean of GRADE = 0.344
Number of cases 'correctly predicted' = 26 (81.2%)
f(beta'x) at mean of independent vars = 0.189
McFadden's pseudo-R-squared = 0.374038
Log-likelihood = -12.8896
Likelihood ratio test: Chi-square(3) = 15.4042 (p-value 0.001502)
Akaike information criterion (AIC) = 33.7793
Schwarz Bayesian criterion (BIC) = 39.6422
Hannan-Quinn criterion (HQC) = 35.7227
Predicted
0 1
Actual 0 18 3
1 3 8
Model 2: Probit estimates using the 32 observations 1-32
Dependent variable: GRADE
VARIABLE COEFFICIENT STDERROR T STAT SLOPE
(at mean)
const -7.45232 2.54247 -2.931
GPA 1.62581 0.693883 2.343 0.533347
TUCE 0.0517288 0.0838903 0.617 0.0169697
PSI 1.42633 0.595038 2.397 0.467908
Mean of GRADE = 0.344
Number of cases 'correctly predicted' = 26 (81.2%)
f(beta'x) at mean of independent vars = 0.328
McFadden's pseudo-R-squared = 0.377478
Log-likelihood = -12.8188
Likelihood ratio test: Chi-square(3) = 15.5459 (p-value 0.001405)
Akaike information criterion (AIC) = 33.6376
Schwarz Bayesian criterion (BIC) = 39.5006
Hannan-Quinn criterion (HQC) = 35.581
Predicted
0 1
Actual 0 18 3
1 3 8
# \end{code}
# \begin{code}
tobit depvar indvars
# \end{code}
# \begin{code}
heckit y X ; d Z
# \end{code}
# \begin{code}
/* multi-line comment
# hello
# hello */
# \end{code}
# \begin{code}
# single-line comment /* hello
# \end{code}
# \begin{code}
ols 1 0 2 3 # estimate the baseline model
# \end{code}
# \begin{code}
/*
# let's skip this for now
ols 1 0 2 3 4
omit 3 4
*/
# \end{code}
# \begin{code}
/* initialize stuff */
series e = 0
scalar beta = 0
matrix V = I(1)
/* proceed with estimation */
gmm
series e = y - x*beta
orthog e ; x
weights V
params beta
end gmm
# \end{code}
# \begin{code}
orthog x ; Z
# \end{code}
# \begin{code}
Model 1: TSLS estimates using the 48 observations 1-48
Dependent variable: lpackpc
Instruments: rtaxso rtax
Heteroskedasticity-robust standard errors, variant HC0
VARIABLE COEFFICIENT STDERROR T STAT P-VALUE
const 9.89496 0.928758 10.654 <0.00001 ***
lravgprs -1.27742 0.241684 -5.286 <0.00001 ***
lperinc 0.280405 0.245828 1.141 0.25401
Model 2: 1-step GMM estimates using the 48 observations 1-48
e = lpackpc - b0 - b1*lravgprs - b2*lperinc
PARAMETER ESTIMATE STDERROR T STAT P-VALUE
b0 9.89496 0.928758 10.654 <0.00001 ***
b1 -1.27742 0.241684 -5.286 <0.00001 ***
b2 0.280405 0.245828 1.141 0.25401
GMM criterion = 0.0110046
# \end{code}
# \begin{code}
set force_hc on
# \end{code}
# \begin{code}
# \documentclass[11pt]{article}
# \usepackage[latin1]{inputenc} %% but see below
# \usepackage{amsmath}
# \usepackage{dcolumn,longtable}
# \begin{document}
# \thispagestyle{empty}
# \end{code}
# \begin{code}
--format="%.4f|%.4f|%.4f|%.4f"
--format="%.4f|%.4f|%.3f|"
--format="%.5f|%.4f||%.4f"
--format="%.8g|%.8g||%.4f"
# \end{code}
# \begin{code}
# \usepackage{ucs}
# \usepackage[utf8x]{inputenc}
# \end{code}
# \begin{code}
list xlist = x1 x2 x3
discrete z1 xlist z2
# \end{code}
# \begin{code}
discrete foo
# now foo is discrete
discrete foo --reverse
# now foo is continuous
# \end{code}
# \begin{code}
nulldata 100
# generate a variable with mean 2 and variance 1
genr x = normal() + 2
# split into 4 classes
genr z = (x>0) + (x>2) + (x>4)
# now declare z as discrete
discrete z
# \end{code}
# \begin{code}
open greene22_2
discrete Z5 # mark Z5 as discrete
dummify Z5
# \end{code}
# \begin{code}
list dlist = dummify(x)
# \end{code}
# \begin{code}
open greene22_2
discrete Z5 # mark Z5 as discrete
list foo = dummify(Z5)
loop foreach i foo
smpl $i --restrict --replace
summary Y
end loop
smpl full
# \end{code}
# \begin{code}
open greene22_2
discrete Z5 # mark Z5 as discrete
ols Y 0 dummify(Z5)
# \end{code}
# \begin{code}
open greene19_1
freq TUCE
discrete TUCE # mark TUCE as discrete
freq TUCE
# \end{code}
# \begin{code}
Read datafile /usr/local/share/gretl/data/greene/greene19_1.gdt
periodicity: 1, maxobs: 32,
observations range: 1-32
Listing 5 variables:
0) const 1) GPA 2) TUCE 3) PSI 4) GRADE
? freq TUCE
Frequency distribution for TUCE, obs 1-32
number of bins = 7, mean = 21.9375, sd = 3.90151
interval midpt frequency rel. cum.
< 13.417 12.000 1 3.12% 3.12% *
13.417 - 16.250 14.833 1 3.12% 6.25% *
16.250 - 19.083 17.667 6 18.75% 25.00% ******
19.083 - 21.917 20.500 6 18.75% 43.75% ******
21.917 - 24.750 23.333 9 28.12% 71.88% **********
24.750 - 27.583 26.167 7 21.88% 93.75% *******
>= 27.583 29.000 2 6.25% 100.00% **
Test for null hypothesis of normal distribution:
Chi-square(2) = 1.872 with p-value 0.39211
? discrete TUCE # mark TUCE as discrete
? freq TUCE
Frequency distribution for TUCE, obs 1-32
frequency rel. cum.
12 1 3.12% 3.12% *
14 1 3.12% 6.25% *
17 3 9.38% 15.62% ***
19 3 9.38% 25.00% ***
20 2 6.25% 31.25% **
21 4 12.50% 43.75% ****
22 2 6.25% 50.00% **
23 4 12.50% 62.50% ****
24 3 9.38% 71.88% ***
25 4 12.50% 84.38% ****
26 2 6.25% 90.62% **
27 1 3.12% 93.75% *
28 1 3.12% 96.88% *
29 1 3.12% 100.00% *
Test for null hypothesis of normal distribution:
Chi-square(2) = 1.872 with p-value 0.39211
# \end{code}
# \begin{code}
xtab ylist ; xlist
# \end{code}
# \begin{code}
xtab xlist
# \end{code}
# \begin{code}
open greene22_2
discrete Z* # mark Z1-Z8 as discrete
xtab Z1 Z4 ; Z5 Z6
# \end{code}
# \begin{code}
Cross-tabulation of Z1 (rows) against Z5 (columns)
[ 1][ 2][ 3][ 4][ 5] TOT.
[ 0] 20 91 75 93 36 315
[ 1] 28 73 54 97 34 286
TOTAL 48 164 129 190 70 601
Pearson chi-square test = 5.48233 (4 df, p-value = 0.241287)
Cross-tabulation of Z1 (rows) against Z6 (columns)
[ 9][ 12][ 14][ 16][ 17][ 18][ 20] TOT.
[ 0] 4 36 106 70 52 45 2 315
[ 1] 3 8 48 45 37 67 78 286
TOTAL 7 44 154 115 89 112 80 601
Pearson chi-square test = 123.177 (6 df, p-value = 3.50375e-24)
Cross-tabulation of Z4 (rows) against Z5 (columns)
[ 1][ 2][ 3][ 4][ 5] TOT.
[ 0] 17 60 35 45 14 171
[ 1] 31 104 94 145 56 430
TOTAL 48 164 129 190 70 601
Pearson chi-square test = 11.1615 (4 df, p-value = 0.0248074)
Cross-tabulation of Z4 (rows) against Z6 (columns)
[ 9][ 12][ 14][ 16][ 17][ 18][ 20] TOT.
[ 0] 1 8 39 47 30 32 14 171
[ 1] 6 36 115 68 59 80 66 430
TOTAL 7 44 154 115 89 112 80 601
Pearson chi-square test = 18.3426 (6 df, p-value = 0.0054306)
# \end{code}
# \begin{code}
matrix A = { 1, 2, 3 ; 4, 5, 6 }
# \end{code}
# \begin{code}
matrix A = { x1, x2, x3 }
# \end{code}
# \begin{code}
matrix A = { x, x^2 }
# \end{code}
# \begin{code}
list xlist = x1 x2 x3
matrix A = { xlist }
# \end{code}
# \begin{code}
matrix A = { dataset }
# \end{code}
# \begin{code}
matrix A = {const}~{dataset}
# \end{code}
# \begin{code}
matrix A = {}
# \end{code}
# \begin{code}
matrix B = A[1,]
matrix B = A[2:3,3:5]
matrix B = A[2,2]
matrix idx = { 1, 2, 6 }
matrix B = A[idx,]
# \end{code}
# \begin{code}
matrix A = { 1, 2, 3; 4, 5, 6; 7, 8, 9 }
matrix B = A[1:2,2:3]
# \end{code}
# \begin{code}
matrix A = { 1, 2, 3; 4, 5, 6; 7, 8, 9 }
matrix A[2:3,2:3] = I(2)
matrix d = { 1, 1, 1 }
matrix A[diag] = d
# \end{code}
# \begin{code}
matrix C = A + k
matrix D = A - k
# \end{code}
# \begin{code}
matrix C = X'Y
# \end{code}
# \begin{code}
matrix C = A/B
# \end{code}
# \begin{code}
matrix C = A .* B
# \end{code}
# \begin{code}
matrix Y = X .* v
# \end{code}
# \begin{code}
matrix C = A ~ B
# \end{code}
# \begin{code}
matrix C = A | B
# \end{code}
# \begin{code}
matrix B = sqrt(A)
# \end{code}
# \begin{code}
matrix a = mnormal(2,3)
a
matrix b = mshape(a,3,1)
b
matrix b = mshape(a,5,2)
b
# \end{code}
# \begin{code}
? a
a
1.2323 0.99714 -0.39078
0.54363 0.43928 -0.48467
? matrix b = mshape(a,3,1)
Generated matrix b
? b
b
1.2323
0.54363
0.99714
? matrix b = mshape(a,5,2)
Replaced matrix b
? b
b
1.2323 -0.48467
0.54363 1.2323
0.99714 0.54363
0.43928 0.99714
-0.39078 0.43928
# \end{code}
# \begin{code}
B = qform(A, X)
# \end{code}
# \begin{code}
matrix B = func(A, &C)
# \end{code}
# \begin{code}
matrix V
matrix E = eigensym(M, &V)
matrix E = eigensym(M, null)
# \end{code}
# \begin{code}
matrix R
matrix Q = qrdecomp(M, &R)
matrix Q = qrdecomp(M, null)
# \end{code}
# \begin{code}
matrix B = func(A, &C, &D)
# \end{code}
# \begin{code}
matrix s = svd(A, &U, &V)
matrix s = svd(A, null, null)
matrix s = svd(A, null, &V)
# \end{code}
# \begin{code}
matrix s = svd(A, &U, &V)
matrix B = (U.*s)*V
# \end{code}
# \begin{code}
matrix B = mols(Y, X, &U)
# \end{code}
# \begin{code}
matrix A = {}
matrix B = A ~ X
# \end{code}
# \begin{code}
matrix u = $uhat
matrix b = m1.$coeff
matrix v2 = m1.$vcv[1:2,1:2]
# \end{code}
# \begin{code}
matrix Pi = $jalpha * $jbeta'
# \end{code}
# \begin{code}
scalar x = 3
matrix x = ones(2,2) # wrong!
# \end{code}
# \begin{code}
scalar x = 3
delete x
matrix x = ones(2,2) # OK
# \end{code}
# \begin{code}
series s = x
series u1 = U[,1]
# \end{code}
# \begin{code}
matrix M = { listname }
# \end{code}
# \begin{code}
matrix M = listname
# \end{code}
# \begin{code}
list Xl = M
# \end{code}
# \begin{code}
delete M
# \end{code}
# \begin{code}
matrix M = mnormal(100,2)
M
print M
# \end{code}
# \begin{code}
set hc_version 2
# \end{code}
# \begin{code}
set hac_kernel parzen
set hac_kernel qs
set hac_kernel bartlett
# \end{code}
# \begin{code}
set hac_lag nw2
# \end{code}
# \begin{code}
set hac_lag 6
# \end{code}
# \begin{code}
set qs_bandwidth 3.5
# \end{code}
# \begin{code}
set hac_prewhiten on
# \end{code}
# \begin{code}
set hac_lag nw3
# \end{code}
# \begin{code}
set pcse on
# \end{code}
# \begin{code}
set pcse off
# \end{code}
# \begin{code}
smpl 1970:1 1979:4
# \end{code}
# \begin{code}
smpl ; 2000:4
# \end{code}
# \begin{code}
smpl 1970:1 2003:4
smpl ; 2000:4
# \end{code}
# \begin{code}
smpl +1 ;
# \end{code}
# \begin{code}
smpl +2 -1
# \end{code}
# \begin{code}
smpl gender=0 --restrict
# \end{code}
# \begin{code}
smpl income>50000 --restrict
# \end{code}
# \begin{code}
smpl --full
# \end{code}
# \begin{code}
smpl income>50000 --restrict --replace
# \end{code}
# \begin{code}
smpl year=1995 --restrict
# \end{code}
# \begin{code}
smpl firm=3 --restrict
# \end{code}
# \begin{code}
smpl 100 --random
# \end{code}
# \begin{code}
genr Y = fracdiff(X,0.5)
# \end{code}
# \begin{code}
genr ct = hpfilt(yt)
genr gt = yt - ct
# \end{code}
# \begin{code}
set bkbp_limits 18 96
set bkbp_k 24
# \end{code}
# \begin{code}
genr index
genr dum = ((index-1) % 30) = 0
# \end{code}
# \begin{code}
genr pmx = pmean(x)
# \end{code}
# \begin{code}
smpl (psd(x) > 0) --restrict
# \end{code}
# \begin{code}
genr xr = resample(x)
# \end{code}
# \begin{code}
? genr p1 = cdf(X, 50, 0.9)
Generated scalar p1 (ID 2) = 8.94977e-35
? genr p2 = pvalue(X, 50, 0.9)
Generated scalar p2 (ID 3) = 1
? genr test = 1 - p2
Generated scalar test (ID 4) = 0
# \end{code}
# \begin{code}
? genr p1 = cdf(X, 50, 30)
Generated scalar p1 (ID 2) = 0.0111648
? genr p2 = pvalue(X, 50, 30)
Generated scalar p2 (ID 3) = 0.988835
? genr test = 1 - p2
Generated scalar test (ID 4) = 0.0111648
# \end{code}
# \begin{code}
genr nmiss_x = sum(missing(x))
# \end{code}
# \begin{code}
genr time
genr x0 = min(zeromiss(time * ok(x)))
# \end{code}
# \begin{code}
? adf 4 x1 --c
Augmented Dickey-Fuller tests, order 4, for x1
sample size 59
unit-root null hypothesis: a = 1
test with constant
model: (1 - L)y = b0 + (a-1)*y(-1) + ... + e
estimated value of (a - 1): -0.216889
test statistic: t = -1.83491
asymptotic p-value 0.3638
P-values based on MacKinnon (JAE, 1996)
? genr pv = $pvalue
Generated scalar pv (ID 13) = 0.363844
? label pv
pv=Dickey-Fuller pvalue (scalar)
# \end{code}
# \begin{code}
matrix X = { dataset }
matrix theta = { 1, 100 }'
scalar J = BFGSmax(theta, ObjFunc(&theta, &X))
# \end{code}
# \begin{code}
function ObjFunc (matrix *theta, matrix *X)
scalar val = ... # do some computation
return scalar val
end function
# \end{code}
# \begin{code}
matrix Jac = fdjac(theta, SumOC(&theta, &X))
# \end{code}
# \begin{code}
function SumOC (matrix *theta, matrix *X)
matrix V = ... # do some computation
return matrix V
end function
# \end{code}
# \begin{code}
x1 = { 1 ; 2 ; 3 }
# perform the transform
f = fft(a)
# perform the inverse transform
x2 = ffti(f)
# \end{code}
# \begin{code}
# define the two polynomials
a = { 1, 0.5, 0, 0 }'
b = { 1, 0.3, -0.8, 0 }'
# perform the transforms
fa = fft(a)
fb = fft(b)
# complex-multiply the two transforms
fc = cmult(fa, fb)
# compute the coefficients of c via the inverse transform
c = ffti(fc)
# \end{code}
# \begin{code}
# define the two polynomials
a = { 1 ; 0.5; 0 ; 0 }
b = { 1 ; 0.3 ; -0.8 ; 0 }
# perform the transforms jointly
f = fft(a ~ b)
# complex-multiply the two transforms
fc = cmult(f[,1:2], f[,3:4])
# compute the coefficients of c via the inverse transform
c = ffti(fc)
# \end{code}
# \begin{code}
genr unitdum
ols y x du_*
# \end{code}
# \begin{code}
font = Times-Roman
fontsize = 16
max = 4.0
min = 0
width = 400
height = 448
numbers = %3.2f
outliers = true
# \end{code}
# \begin{code}
salary (GENDER=1) salary (GENDER=0)
# \end{code}
# \begin{code}
scalar alpha = 1
scalar p = 1
mle logl = p*ln(alpha * x) - lngamma(p) - ln(x) - alpha * x
end mle
# \end{code}
# \begin{code}
alpha = 1
p = 1
# \end{code}
# \begin{code}
scalar alpha = 1
scalar p = 1
mle logl = p*ln(ax) - lngamma(p) - ln(x) - ax
series ax = alpha*x
params alpha p
end mle
# \end{code}
# \begin{code}
scalar m = mean(x)
scalar alpha = m/var(x)
scalar p = m*alpha
mle logl = p*ln(ax) - lngamma(p) - ln(x) - ax
series ax = alpha*x
params alpha p
end mle
# \end{code}
# \begin{code}
scalar m = mean(x)
scalar alpha = m/var(x)
scalar p = m*alpha
mle logl = check ? p*ln(ax) - lngamma(p) - ln(x) - ax : NA
series ax = alpha*x
scalar check = (alpha>0) & (p>0)
params alpha p
end mle
# \end{code}
# \begin{code}
open djclose
series y = 100*ldiff(djclose)
scalar mu = 0.0
scalar omega = 1
scalar alpha = 0.4
scalar beta = 0.0
mle ll = -0.5*(log(h) + (e^2)/h)
series e = y - mu
series h = var(y)
series h = omega + alpha*(e(-1))^2 + beta*h(-1)
params mu omega alpha beta
end mle
# \end{code}
# \begin{code}
nulldata 1000
genr x1 = normal()
genr x2 = normal()
genr x3 = normal()
genr ystar = x1 + x2 + x3 + normal()
genr y = (ystar > 0)
scalar b0 = 0
scalar b1 = 0
scalar b2 = 0
scalar b3 = 0
mle logl = y*ln(P) + (1-y)*ln(1-P)
series ndx = b0 + b1*x1 + b2*x2 + b3*x3
series P = cnorm(ndx)
params b0 b1 b2 b3
end mle --verbose
# \end{code}
# \begin{code}
mle logl = y*ln(P) + (1-y)*ln(1-P)
series ndx = b0 + b1*x1 + b2*x2 + b3*x3
series P = cnorm(ndx)
series tmp = dnorm(ndx)*(y/P - (1-y)/(1-P))
deriv b0 = tmp
deriv b1 = tmp*x1
deriv b2 = tmp*x2
deriv b3 = tmp*x3
end mle --verbose
# \end{code}
# \begin{code}
series tmp = dnorm(ndx)*(y/P - (1-y)/(1-P))
print tmp
# \end{code}
# \begin{code}
setobs 20 9:1 --special
# \end{code}
# \begin{code}
setobs 1 1 --cross-section
genr sortkey = -obs
dataset sortby sortkey
setobs 1 1950 --time-series
# \end{code}
# \begin{code}
genr x = sortby(-obs, x)
# \end{code}
# \begin{code}
list X = x1 x2 x3
genr sel = ok(X)
smpl sel --restrict
# \end{code}
# \begin{code}
matrix vd = values(d)
m = rows(vd)
loop for i=1..m
scalar sel = vd[i]
smpl (d=sel) --restrict --replace
ols y const x
end loop
smpl full
# \end{code}
# \begin{code}
genr d = (t="1984:2")
# \end{code}
# \begin{code}
genr DIta = (t="Italy")
# \end{code}
# \begin{code}
alpha = 0.9
theta = -0.5
series e = normal()
series y = 0
series y = alpha * y(-1) + e + theta * e(-1)
# \end{code}
# \begin{code}
series y = d ? x : z
# \end{code}
# \begin{code}
list X = x1 x2 x3
list Z = z1 z2
list dZ = null
loop foreach i Z
series d$i = d * $i
list dZ = dZ d$i
end loop
ols y X Z d dZ
# \end{code}
# \begin{code}
smpl full
genr time
genr minute = int(time/60) + 1
genr second = time % 60
setobs minute second --panel
genr rv = psd(y)^2
setobs 1 1
smpl second=1 --restrict
store foo rv
# \end{code}
# \begin{code}
ols C 0 Y
genr alpha = $coeff(0)
genr beta = $coeff(Y)
genr gamma = 1
# \end{code}
# \begin{code}
ols y 0 x1 x2
genr alpha = $coeff(0)
genr beta = $coeff(x1)
# \end{code}
# \begin{code}
set nls_toler .0001
# \end{code}
