Time Series Analysis with lags and non temporally consecutive values

#1
This is what i'm trying to do. Ive got a set of news surprise data (announced value-forecast) at the times of release. For example, I have 12 GDP announcements made at 8:30 quarterly spanning three years. I'm regressing the exchange rate against the news release to quantify the effect the news release has on the rate. I'm trying to do this using these two models. The most basic is:

Rt= βkSt + εt

Where Rt is returns and St is the GDP news. Am I right in creating a GDP variable with its figures placed in the matrix at the times it was released. Then, regressing returns against the variable should give me how the news effects the exchange rate, right?

Then, the more complex model is:

Rt = β0 + (SUMMATION)βiRt-i + (SUMMATION) βk,t Sk,t-j + εt (x)

So as to include the persistence of the effects over time, making j=5 or something to test how the effects dampen out over the 25 minutes following the release. I am concerned I am not using stata correctly to do this. When I try to include lagged variables of the news it says "no observations".

My issue is what to do with the news variables as they are not strictly time series data as they do not have temporally consecutive values. Do I just put them in where they are and regress or is there something better. A paper by Almeida, Goodhart and Payne called “The Effects of Macroeconomic News on High Frequency Exchange Rate Behaviour” does what I am trying to do.

Any suggestions from anybody will be much appreciated.