Making predictions with a distributed lag model I have a question regarding making predictions from distributed lag models. Suppose, I have a simple linear distributed lag model of the form:

$$y\left(t\right)= \sum^{k}_{i=0}\beta_{i}x\left(t-i\right) + \epsilon\left(t\right)$$

Assuming co-linearity is not an issue. Suppose, I have many data points along the temporal and spatial domain for the explanatory variable $x$. However, I only have data for one time instance for the response variable but I have many data points in a spatial domain. Assume that there is homogeneity in the spatial domain.

The variables have a spatial domain; however, I am ignoring this and assuming homogeneity int the spatial domain for both $x$ and $y$. Also, many values of $y$ can correspond to a single value of $x$.

Is it possible to use the model for prediction? In the sense that one could take the lag coefficients and say at the initial time the only effect of the explanatory variable on the response variable is $\beta_{o}$ and at some time in the future, $t=n$, the only effect of the explanatory variable on the response variable is $\beta_{n}$.  

Thanks in advance for you time and consideration.
So the short answer is yes, you can generate some predictions based on your model. Whether it will generate good predictions is another question. Basically you are only using the spatial variation to estimate your model and no time variation. So your predictions would most likely be very poor unless the relationship is very stable over time. There's also no way of knowing which lagged values of the explanatory variable would be useful in prediction, unless there is some spatial structure that you can use (i.e. some regions are leading indicators for other regions)