Time Series Forecasting with R Help

Assuming you have taken the required math, econ including metrics, and stats courses what is the best way to get up to speed on Times series forecasting with R? Have messed around with datacamp, O Reilly, FPP etc but can't seem to make the skills "stick."Do you stop and make sure you understand every concept thoroughly before moving on ie all the underlying math?When do you start trying to implement a project?How do you make sure you remind yourself of statistical principles when you're deep focused on the coding aspects?Looking for a better skills refreshment template if any have successfully done this

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Neither EViews nor STATA requires you to "code" much and think about why your code is/isn't working - you can just the menus and stuff and some commands to build your model, so you can focus on the actual econometric modelling that you're doing. Note that I've only ever used STATA for non-timeseries modelling, I've used EViews for time series.

Also I didn't actually read your original question properly, so let me try and address it:

I think it is helpful to understand why you're doing what you're doing, because doing a procedure for the sake of doing it might not always work/you might miss things which make you end up with a misspecified model, or you're not carrying out the correct tests, for example. 

 

Wait is there ANYONE in industry/ buyside who uses STATA? Only academic economists use it

I assume why're talking about time-series for HF, since we're on this forum?

I guess you could regress shit and do factor analysis for buyside, but that's still so primitive

 

Idk why people don't use STATA, it's very user friendly and easy to use... People do use EViews though, particularly for time series. I think it was developed in the industry as well

 
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I would suggest learning the underlying theory because it is very beautiful but you should do that as a side project not related to actually implementing a time series model. I say that because the theory is very dense and fairly complicated which means that you will invest a lot of time but despite that, knowing it will not necessarily make you a better modeler unless your end goal is to write an academic paper explaining a new method you invented to fit curves. 

For actually implementing a model just learn

1) What is a time series (and, most importantly, what it is not. I.e. what kind of data can this paradigm actually fit?)

2) The common statistical methods for fitting data (and here just learn the R algorithms, not what they are based on)

3) The common fit measures to judge the fit (and, again, just learn the R formula and what it is supposed to represent)

And, regardless of how cool the statistical theory is, nothing beats actual backtesting in the real world. As such, learning more stats will not necessarily make you a better modeler. It will make you a better academic but that is not precisely what you want I presume. You can probably go through those 3 points above in a day and that will allow you to start actually playing around with real time series. You will not be an expert but the benefit is that now that you can actually do it, you can start developing intuition by doing it repeatedly. That developed intuition will make it easier for you to understand the theory and vice-versa. 

 

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