In the following result, the adf.test shows that the series is non stationary, while ur.df shows it is stationary (choose the lags based on decreasing from lags=5 until the lag coefficient becomes significant).

Should I trust that the series is stationary based on a=5% since the p value of z.lag.1 in ur.df is 0.0299<0.05?

Augmented Dickey-Fuller Test


data: resid(fit1) Dickey-Fuller = -1.6595, Lag order = 3, p-value = 0.7089 alternative hypothesis: stationary

summary(ur.df(resid(fit1),lags=2))

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# Augmented Dickey-Fuller Test Unit Root Test #

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Test regression none

Call:
lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)

Residuals:
Min       1Q   Median       3Q      Max
-4944058 -1028037  -388697   847454  4089084

Coefficients:
Estimate Std. Error t value Pr(>|t|)
z.lag.1      -0.4614     0.2047  -2.254   0.0299 *

z.diff.lag1  -0.1785     0.1808  -0.987   0.3295

z.diff.lag2  -0.3309     0.1527  -2.167   0.0364 *

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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1762000 on 39 degrees of freedom
Multiple R-squared:  0.4053,    Adjusted R-squared:  0.3596
F-statistic:  8.86 on 3 and 39 DF,  p-value: 0.0001328

Value of test-statistic is: -2.2543

Critical values for test statistics:
1pct  5pct 10pct
tau1 -2.62 -1.95 -1.61