I currently have a panel data set that contains the quarterly increment of loans initiated from more than 300 cities in China over the period from 2011Q1 to 2020Q2. I want to examine the impact of COVID-19 on lending activities. Here below is the nation-level aggregated increment of loans on a quarter base, so it is a time series.
To avoid serious multicollinearity, I cannot use the time fixed effects and event dummy at the same time in my model. However, I do want to hold the sudden change happened in 2019Q3 to improve the model fitting. I doubt it is a structural break by just "looking", but I am unfamiliar with this technique. In my case, if it indeed is a structural break, how to detect it convincingly and adjust it? Is the structural break represented by a time-point dummy like a dummy(time >=2019Q3)?
I am also curious about a more general question. Do time fixed effects give more details than structural break? In other words, is the information told by structural break a subset of time fixed effects?