Reddit reviews Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Both time series and regression are not strictly econometric methods per se, and there are a range of wonderful statistics textbooks that detail them. If you're looking for methods more closely aligned with econometrics (e.g. difference in difference, instrumental variables) then the recommendation for Angrist 'Mostly Harmless Econometrics' is a good one. Another oft-prescribed econometric text that goes beyond Angrist is Wooldridge 'Introductory Econometrics: A Modern Approach'.

For a very well considered and basic approach to statistics up to regression including an excellent treatment of probability theory and the basic assumptions of statistical methodology, Andy Field (and co's) books 'Discovering Statistics Using...' (SPSS/SAS/R) are excellent.

Two excellent all-rounders are Cohen and Cohen 'Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences' and Gelman and Hill 'Data Analysis Using Regression and Multilevel/Hierarchical Modelling' although I would suggest both are more advanced than I am guessing you need right now.

For time series I can recommend Rob Hyndman's book/s on forecasting (online copy freely available)

For longitudinal data analysis I really like Judith Singer's book 'Applied Longitudinal Data Analysis'.

It sounds however as if you're looking for a bit of a book to explain why you would want to use one method over another. In my experience I wanted to know this when I was just starting. It really comes down to your own research questions and the available data. For example I had to learn Longitudinal/fixed/random effects modelling because I had to do a project with a longitudinal survey. Only after I put it into practice (and completed my stats training) did I come to understand why the modelling I used was appropriate.

I can recommend Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith Singer & John Willett.

The second half of the book considers survival analysis, and R code for the examples from each chapter of the book are available here -- in addition to code for SAS, SPSS Stata, etc.

Full disclosure: I have only worked through the first half of the book so far (growth curve modeling), but the text is very complete and the code on that webpage has been really helpful. I imagine the section on survival analysis is similarly helpful.

IMO repeated measures and longitudinal data are extremely underappreciated topics for data scientists, which you encounter if you work with any kind of data where you record data on "subjects" or "users" over time. The most accessible and best resource I have seen on this topic is Applied Longitudinal Data Analysis by Singer and Willett, which is written for a graduate-level social science audience and has a light touch on the math but goes heavy on building intuition for random effects models and survival analysis. There is a cache of examples in R for it here. There are more mathematical treatments of this from a biostatistics perspective, such as Analysis of Longitudinal Data, but I would start with Singer and Willett.

This is the best, most accessible book I've read on the topic. Also UCLA has a wonderful companion website on the topic. The book is straight forward and the website will give you all of the commands that you need for whatever statistical package you are using. You can simply substitute the variable names for the data set you are using. If you need a more advanced analysis and use STATA try this book. I use it weekly. The University of Bristol has a video series and sweet website on the topic as well.