Hello List,
My question may be a too simple question to be asked on the list, but I hope some of you may be kind enough to help me out.
I am trying to replicate somebody else's time series analysis that was conducted in SAS (proc autoreg, in particular). Below is the SAS code that I am trying to replicate, followed by Stata code that I believe is doing the same analysis.
Although the regression coefficient estimates by these codes are virtually the same, the direction of autoregressive error coefficients are in the opposite.
Am I doing something wrong? Do I need to specify certain options to get the same results?
**** SAS code ***
proc autoreg data=test.japan09 all method=ml;
model murdr = welf gini unemp drate fl urbanper mpr20_29 clr1/ nlag=(1 2) dw=6 dwprob;
run;
*** Stata code ***
arima murdr welf gini unemp drate fl urbanper mpr20_29 clr1, ar(1/2)
*** SAS output ****
Standard Approx
Variable DF Estimate Error t Value Pr > |t| Variable Label
Intercept 1 -0.2291 1.3726 -0.17 0.8683
WELF 1 0.0901 0.0139 6.50 <.0001
GINI 1 2.5837 1.0148 2.55 0.0150
UNEMP 1 0.0807 0.0464 1.74 0.0901
DRATE 1 -0.1187 0.1509 -0.79 0.4363
FL 1 -0.009282 0.008776 -1.06 0.2967
URBANPER 1 -0.0159 0.007080 -2.25 0.0302
MPR20_29 1 0.1578 0.0270 5.84 <.0001
CLR1 1 0.003780 0.003500 1.08 0.2867
AR1 1 -0.4149 0.1392 -2.98 0.0049
AR2 1 0.5435 0.1374 3.96 0.0003
*** Stata output***
ARIMA regression
Sample: 1951 - 2000 Number of obs = 50
Wald chi2(10) = 7883.00
Log likelihood = 61.32109 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| OPG
murdr | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
murdr |
welf | .0900617 .0143372 6.28 0.000 .0619613 .118162
gini | 2.583805 .8179648 3.16 0.002 .9806232 4.186986
unemp | .0807053 .0521775 1.55 0.122 -.0215608 .1829714
drate | -.1186688 .1867984 -0.64 0.525 -.4847869 .2474493
fl | -.009291 .0096179 -0.97 0.334 -.0281418 .0095598
urbanper | -.0159288 .0061898 -2.57 0.010 -.0280606 -.0037971
mpr20_29 | .1577909 .0355243 4.44 0.000 .0881645 .2274172
clr1 | .003777 .0050106 0.75 0.451 -.0060437 .0135977
_cons | -.2280505 1.405894 -0.16 0.871 -2.983553 2.527452
-------------+----------------------------------------------------------------
ARMA |
ar |
L1. | .4148975 .1494673 2.78 0.006 .121947 .707848
L2. | -.5435063 .1329498 -4.09 0.000 -.8040832 -.2829295
-------------+----------------------------------------------------------------
/sigma | .0704317 .0093342 7.55 0.000 .0521371 .0887263
------------------------------------------------------------------------------
Thank you,
George
***************************************
George Kikuchi, Ph.D.
National Research Institute of Police Science
Department of Criminology and Behavioral Sciences
Crime Prevention Section
6-3-1 Kashiwanoha
Kashiwa-shi, Chiba 277-0882
Japan
TEL: +81-4-7135-8001 ext.2641
FAX: +81-4-7133-9184
e-mail: [email protected]
***************************************
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
