This might be a bit of a newbish question, but I recently picked up a forecasting project at my job, and I'm trying to figure out whether it makes sense to run a panel regression like a Fixed Effects or Random Effects Model, or if a simple cross-sectional OLS would suffice.
My data consists of three separate columns ordered by date, each consisting of test scores a given person received at three different time periods (see below). For all intents and purposes, each of the three tests are the same; that is, the specific questions might differ, but the overall structure and content is consistent, so the scores are likely highly correlated, and are presumably increasing over time.
Person ID/Index Test 1 Test 2 Test 3 ----------------|--------|--------|--------| 1 | 65 | 70 | 81 2 | 55 | 45 | 55 3 | 90 | 95 | 93 4 | 78 | 82 | 93 ... ... ... ...
My instinct tells me that this is a panel data problem, since we are looking at test scores both cross-sectionally across individual test-takers, and longitudinally over three testing periods, but I'm kind of second-guessing myself since my forecasting experience is pretty limited, and I haven't really worked with panel data in such a "clean" format before. I'm planning to do my analyses in Python or R, and if I'm understanding the documentation correctly, most relevant panel packages expect the data to be in a "long" format.
Would anyone care to weigh in? Is my instinct correct, or would a basic OLS do the trick?