For $t$ fixed, the count is proportional to $lambda^n$, where $lambda = 2 cos fracpi{2t+2}$ is the principal eigenvalue of the adjacency matrix of the path with $2t+1$ vertices. The all-positive (Perron-Frobenius) eigenvector corresponding to $lambda$ is
$$bigg(sin frac{pi}{2t+2}, sin frac{2pi}{2t+2},sin frac{2pi}{2t+2},dots,sin frac{(2t+1)pi}{2t+2}bigg).$$
Since $-lambda$ is also an eigenvalue, the stable behavior of the distribution of endpoints of paths which stay in $[-t,t]$ is an oscillation between the odd entries
$$bigg(sin frac{pi}{2t+2}, 0,sin frac{3pi}{2t+2},0,dots,sin frac{(2t-1)pi}{2t+2},0,sin frac{(2t+1)pi}{2t+2}bigg).$$
and even entries
$$bigg(0,sin frac{2pi}{2t+2}, 0,sin frac{4pi}{2t+2},0,cdots ,0,sin frac{2tpi}{2t+2},0bigg).$$
The exact count of paths staying in $[-t,t]$ is a sum of signed binomial coefficients.
The number of paths from $0$ to $i$ is 0 if $n not equiv i ~mod 2$, and $n choose (npm i)/2$ when $n equiv i ~mod 2$.
The number of paths which never leave $[-t,t]$ from $0$ to $i in [-t,t]$ with $n equiv i ~mod 2$ is
$$ sum_{jin mathbb Z} (-1)^j {nchoose (n +i)/2 + j(t+1)}$$
by the reflection principle applied to the group of isometries of $mathbb R$ generated by reflecting about $t+1$ and $-t-1$.
If you sum over all $i in [-t,t]$, then when $n$ is even, you get a signed sum of binomial coefficients with $t+1$ positive signs in a row alternating with $t+1$ negative signs in a row. If $n$ is odd, then you get $t$ positive signs in a row, skip a term (give it a coefficient of $0$ instead of $pm 1$), then $t$ negative signs in a row, skip a term, etc.
For example, for $n=100, t=2,$ the number of paths is
$$ ... +{100choose 43} + {100choose 44} + {100 choose 45} - {100 choose 46} - {100 choose 47} - {100choose 48} + {100choose 49} + {100 choose 50} + {100choose 51} - ...$$
For $n=101, t=2,$ the number of paths is
$$ ... +{101choose 44} + {101choose 45} - {101choose 47} - {101 choose 48} + {101choose 50} + {101choose 51} - {101choose 53} - {101choose 54} + ...$$
These can be summed using the techniques in the answers to the Binomial distribution parity question.
A lot more can be said when $t$ varies, but the answers are more complicated. For $t$ slowly increasing, as $csqrt[3]n$, there is enough time for the distribution to stabilize (for each parity) at a given value of $t$, since the ratio between the magnitudes of the largest two eigenvalues and the magnitudes of the next two is about $1+c/t^2$, and the principal eigenvectors have a small $L^1$ distance for adjacent values of $t$. You should pick up a constant factor for each transition. In other words, the number of paths when you spend at least $n_t gt c t^2$ steps at a given $t$ should be
$$C prod_{t le t_{max}} (2 cos frac{pi}{2t+2})^{n_t}$$
where $C$ is between some functions $f_{lower}(t_{max}) lt C lt f_{upper}(t_{max})$ which does not depend on the values of $n_t$. I don't think the $n_t gt c t^2$ condition is sharp for this behavior. Something like $n_t gt c t^2/log t$ should work, too. The geometry of the eigenvectors for adjacent values of $t$ lets you estimate $f_{lower}$ and $f_{upper}$.
For $t$ more rapidly increasing, different behaviors occur. By the law of the iterated logarithm, if $t$ increases as $t(n) = sqrt {(2-epsilon) n loglog n},$ random paths will almost surely violate the constraint. I think there are precise versions of the law of the iterated logarithm which may tell you when a positive proportion of random paths do not violate the constraint. I would guess that if $t(n) = sqrt{(2+epsilon) n loglog n}$ then a positive percentage of random paths won't violate the constraint.