*
* Example 5.2
* AR1.PRG
*
cal 1959
all 1998:1
open data ar1.prn
data(format=prn,org=columns)
*
* OLS regression
*
linreg y
# constant x
*
* First difference regression. This may be the best choice if the
* autocorrelation coefficient is close to 1.
*
set dx = x-x{1}
set dy = y-y{1}
linreg dy
# dx
*
* AR1 regression using several methods. HILU and CORC should give almost
* identical answers unless there are multiple roots.
*
ar1(method=hilu) y
# constant x
ar1(method=corc) y
# constant x
ar1(method=maxl) y
# constant x
*
* OLS with Newey-West standard errors. This allows for autocorrelation
* of up to four lags.
*
linreg(robusterrors,lags=4,lwindow=neweywest) y
# constant x