---------------------------------------------------------------------------------------------------------- . tsset year Time variable: year, 1954 to 2003 Delta: 1 year . regress i3 inf def Source | SS df MS Number of obs = 50 -------------+---------------------------------- F(2, 47) = 41.71 Model | 237.12163 2 118.560815 Prob > F = 0.0000 Residual | 133.60322 47 2.8426217 R-squared = 0.6396 -------------+---------------------------------- Adj R-squared = 0.6243 Total | 370.72485 49 7.56581327 Root MSE = 1.686 ------------------------------------------------------------------------------ i3 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- inf | .6826947 .0834686 8.18 0.000 .5147776 .8506118 def | .2780833 .1267186 2.19 0.033 .0231583 .5330082 _cons | 2.09418 .4342994 4.82 0.000 1.220482 2.967878 ------------------------------------------------------------------------------ . * Jääkide autokorrelatsiooni testimine, osa 3. . estat bgodfrey, lags(1 2 3) Breusch–Godfrey LM test for autocorrelation --------------------------------------------------------------------------- lags(p) | chi2 df Prob > chi2 -------------+------------------------------------------------------------- 1 | 22.481 1 0.0000 2 | 22.967 2 0.0000 3 | 22.969 3 0.0000 --------------------------------------------------------------------------- H0: no serial correlation * Kohandatud standardvead, osa 4. . regress i3 inf def, vce(robust) Linear regression Number of obs = 50 F(2, 47) = 32.11 Prob > F = 0.0000 R-squared = 0.6396 Root MSE = 1.686 ------------------------------------------------------------------------------ | Robust i3 | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- inf | .6826947 .0943157 7.24 0.000 .4929559 .8724335 def | .2780833 .1427765 1.95 0.057 -.009146 .5653125 _cons | 2.09418 .3624601 5.78 0.000 1.365004 2.823356 ------------------------------------------------------------------------------ * ARDL mudel, osa 5. . regress i3 L.i3 inf L.inf def L.def Source | SS df MS Number of obs = 49 -------------+---------------------------------- F(5, 43) = 56.01 Model | 304.448305 5 60.889661 Prob > F = 0.0000 Residual | 46.745295 43 1.08709988 R-squared = 0.8669 -------------+---------------------------------- Adj R-squared = 0.8514 Total | 351.1936 48 7.31653333 Root MSE = 1.0426 ------------------------------------------------------------------------------ i3 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- i3 | L1. | .6842309 .0953752 7.17 0.000 .4918885 .8765733 | inf | --. | .3751373 .1051518 3.57 0.001 .1630785 .5871961 L1. | -.0726989 .1342432 -0.54 0.591 -.343426 .1980283 | def | --. | -.3675754 .1518327 -2.42 0.020 -.6737753 -.0613755 L1. | .3911332 .1372275 2.85 0.007 .1143875 .6678789 | _cons | .4662993 .350945 1.33 0.191 -.2414488 1.174047 ------------------------------------------------------------------------------ . regress i3 L.i3 inf def L.def Source | SS df MS Number of obs = 49 -------------+---------------------------------- F(4, 44) = 71.08 Model | 304.129489 4 76.0323722 Prob > F = 0.0000 Residual | 47.0641114 44 1.06963889 R-squared = 0.8660 -------------+---------------------------------- Adj R-squared = 0.8538 Total | 351.1936 48 7.31653333 Root MSE = 1.0342 ------------------------------------------------------------------------------ i3 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- i3 | L1. | .6573911 .0808296 8.13 0.000 .4944896 .8202925 | inf | .3309776 .0658537 5.03 0.000 .1982582 .463697 | def | --. | -.4104138 .1285535 -3.19 0.003 -.6694963 -.1513312 L1. | .4259267 .1202804 3.54 0.001 .1835175 .6683359 | _cons | .516069 .3359667 1.54 0.132 -.1610274 1.193165 ------------------------------------------------------------------------------ * Jääkide autokorrelatiooni testimine, osa 7 . estat bgodfrey, lags(1 2 3) Breusch–Godfrey LM test for autocorrelation --------------------------------------------------------------------------- lags(p) | chi2 df Prob > chi2 -------------+------------------------------------------------------------- 1 | 0.002 1 0.9658 2 | 0.077 2 0.9623 3 | 0.435 3 0.9329 --------------------------------------------------------------------------- H0: no serial correlation * Heteroskedastiivsuse testimine, kas on ARCH efekt. Osa 8. . estat archlm, lags(1 2 3) LM test for autoregressive conditional heteroskedasticity (ARCH) --------------------------------------------------------------------------- lags(p) | chi2 df Prob > chi2 -------------+------------------------------------------------------------- 1 | 0.176 1 0.6746 2 | 0.686 2 0.7097 3 | 0.747 3 0.8621 --------------------------------------------------------------------------- H0: no ARCH effects vs. H1: ARCH(p) disturbance ----------------------------------------------------------------------------------------------------------