Stata can set equality constraints, but you cannot set inequality constraints explicitly. Typically, implicit constraints can be set up by reparameterizing the model, e.g., the constraint that a variance is non-negative can be set by writing out the model in terms of
sigma and then only using
sigma^2 in the likelihood/GMM objective function/moments.
Also typically, if the free unconstrained estimates are significantly out of range, then the model is likely misspecified: you don't have the right regressors, you don't have enough lags, your error distribution is wrong, whatever. By squeezing the parameter range into the "proper" one, you are sweeping the misspecification problem under the carpet, and only make things worse. I am however stressing "significantly", as you could sort of proceed like this: (1) estimate the model with free parameters, (2)
nlcom that they are on the boundary (=1 if that makes economic sense); (3) restrict the parameters by using the standard linear constraint command
constraint and re-estimating the model. That is still a marginal procedure as it loses the control over type I error and introduces strange mixtures of distributions; see literature on pre-test estimators.