1 AR1不显著（10%）
The insignificance of the AR1 test for no serial correlation in the first-differenced errors indicates that there is strong serial correlation in the level errors which in turn is a sign of model misspecification.
2 所有x都是内生的？
Essentially, you are assuming that all of your regressors are endogenous. While in theory it is possible to instrument all of them, in practice that hardly ever yields reasonable results (in particular in small samples). You are demanding too much from your data and would be better off imposing some stronger assumptions about the exogeneity of your regressors (besides the lagged dependent variable).
3 常数项被省略
It is a story about perfect collinearity of the constant with your time dummy variables. Sometimes Stata drops the constant and sometimes one of the time dummies instead.
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4 Hansen p值为1
The Hansen p-value of 1.0000 is a clear indication that your model suffers from severe problems, in particular a too-many-instruments problem.
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5 Missing AR 1 and 2
You have an insufficient number of observations per group. There is no way to compute autocorrelation tests for a first-differenced model with only 2 observations.
6 time dummies
When specifying time dummies, note that there is a bug in xtabond2 that yields incorrect degrees of freedoms for the overidentification tests when some of these dummies are reported as omitted. You should either specify the dummies manually without factor notation or use the xtdpdgmm command instead; see below.)
xtdpdgmm has the option teffects that automatically adds the relevant time dummies.
More on GMM estimation of linear dynamic panel data models:
XTDPDGMM: new Stata command for efficient GMM estimation of linear (dynamic) panel models
系数变化-静动态模型
静态模型中系数显著，
The coefficient of the lagged dependent variable is close to 1. This means that a large amount of variation is explained by history dependence rather than the other explanatory variables. It is not uncommon in these situations that explanatory variables lose statistical significance, in particular if they are themselves highly persistent such that their effect becomes hard to distinguish from the the autoregressive part.
In any case, your number of instruments appears to be too high which could also effect the precision of your estimates.
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