  Stein's formula states that a random variable of the form $z^\top f(z) -
\text{div} f(z)$ is mean-zero for functions $f$ with integrable gradient. Here,
$\text{div} f$ is the divergence of the function $f$ and $z$ is a standard
normal vector. This paper aims to propose a Second Order Stein formula to
characterize the variance of such random variables for all functions $f(z)$
with square integrable gradient, and to demonstrate the usefulness of this
formula in various applications.
  In the Gaussian sequence model, a consequence of Stein's formula is Stein's
Unbiased Risk Estimate (SURE), an unbiased estimate of the mean squared risk
for almost any estimator $\hat\mu$ of the unknown mean. A first application of
the Second Order Stein formula is an Unbiased Risk Estimate for SURE itself
(SURE for SURE): an unbiased estimate {providing} information about the squared
distance between SURE and the squared estimation error of $\hat\mu$. SURE for
SURE has a simple form as a function of the data and is applicable to all
$\hat\mu$ with square integrable gradient, e.g. the Lasso and the Elastic Net.
  In addition to SURE for SURE, the following applications are developed: (1)
Upper bounds on the risk of SURE when the estimation target is the mean squared
error; (2) Confidence regions based on SURE; (3) Oracle inequalities satisfied
by SURE-tuned estimates; (4) An upper bound on the variance of the size of the
model selected by the Lasso; (5) Explicit expressions of SURE for SURE for the
Lasso and the Elastic-Net; (6) In the linear model, a general semi-parametric
scheme to de-bias a differentiable initial estimator for inference of a
low-dimensional projection of the unknown $\beta$, with a characterization of
the variance after de-biasing; and (7) An accuracy analysis of a Gaussian Monte
Carlo scheme to approximate the divergence of functions $f: R^n\to R^n$.
