* * Enders, Applied Econometric Time Series, 4th edition * Example from Chapter 4, pages 226-227 * HEGY test * open data quarterly.xls calendar(q) 1960:1 data(format=xls,org=columns) 1960:01 2012:04 m1nsa m2nsa cpinsa * set trend = t * set y = log(m1nsa) set ysdiff = y-y{4} set y1t = y+y{1}+y{2}+y{3} set y2t = -y+y{1}-y{2}+y{3} set y3t = -y+y{2} seasonal seasons * * Pick the lag length on the seasonal differences by general-to-specific * with maximum lags of 12 and a significance level of .05. * stwise(method=gtos,force=9,slstay=.05) ysdiff # constant trend y1t{1} y2t{1} y3t{1 2} seasons{0 to -2} ysdiff{1 to 12} * * Rerun with the chosen number of lags. (The estimates will be slightly * different because the previous regression uses the range that allows * for 12 lags). Note that the coefficients on the deterministic * variables depend upon the precise way in which the seasonal dummies * are generated; that will not, however, have any effect on the other * coefficients. * linreg ysdiff # constant trend y1t{1} y2t{1} y3t{1 2} seasons{0 to -2} ysdiff{1 to 8} compute forunit=%tstats(3) compute forsemiannual=%tstats(4) exclude(noprint) # y3t{1 2} compute forseasonal=%cdstat * disp "HEGY Test Statistics" disp "Non-Seasonal Unit Root" @30 ###.### forunit disp "Semi-Annual Unit Root" @30 ###.### forsemiannual disp "Annual Unit Root" @30 ###.### forseasonal * * The @HEGY procedure will do the test. Each line in the output has a * different set of deterministic variables. The one which matches the * result above is the last line (Intercept, Seassonal Dummies and Trend). * @hegy(lags=8) y