Cross sectional data - use set index to differentiate for panel data; fixed effect is a tool to analyze panel data (don't use more than 2 indexes); use time as the second variable
If you love Time Series, you should go for Machine Learning. They are competitors. I might be biased, but my view is that Machine Learning is literally eating Time Series Econometrics. In Forecasting problems, it was a time that Time Series Econometrician used to reign. Now, Neural Network dominates completely.
@@bacharahmad2277 Yes, I do recommend. I will teach again Machine Learning in some of my courses. I intend to use the Machine Learning modules of Dataquest.
That is a pretty advanced video! If you allow me to give feedback, I would recommend splitting the video into two shorter videos: the first would be the theoretical explanation of the differences between time series, cross-section, and panel data and the second would be the application on Python and documentation examples. The first video will be suitable for all users while the second will be a little bit more specific to data science students
I agree with you. I recorded this video, because Stephanie asked me the difference among Python Series, Dataframe, and Panel. I posted this same video under different title. I have a video comparing only two categories and not three.
Thanks a million! Do more work in-line with the fundamentals, this domain is a fast moving one and lot of assumptions are made without understanding the fundamental difference between type of techniques and when is best to use one over the other.
So when data of many subjects are observed at the same time they're called cross section and when data contains measurement over multiple time periods it's called panel data? Would that be accurate to try to remember it that way?
Panel data is when you observe the same entities (people, firms, countries) over different period of time. It must be the same entities, otherwise it will not be a panel data.
thank you, this is excellent! would you characterize Panel Data as essentially a time series for multiple individuals/entities/etc.? or it is more nuanced? because it seems to me like this type of data would allow us to compare the change in a given variable across time for many individuals which would be the same as if we had time series data for each individual and we can draw comparisons, is that more or less correct?
There is an important nuance. A crucial characteristic of panel data is that the same unities of analysis (ex: same people, firms, countries, etc.) are observed over time. The consequence is that you can eliminate confound factors that don't vary over time.
Panel Data: Observe cross-sections of the same individuals at different points in time. Ex: National Longitudinal Survey of Youth (NLSY). Pooled Cross-Section data: Randomly sampled cross-sections of individuals at different points in time. The individuals are different at different points in time. Ex: Current population survey (CPS). Panel Data is far superior to eliminate confound factors that don't' vary over time than Pooled Cross-Section data.
Cross sectional data - use set index to differentiate for panel data; fixed effect is a tool to analyze panel data (don't use more than 2 indexes); use time as the second variable
Nice summary.
Thank you for this simple explanation
I am looking forward to using this for research purposes and analytic related projects!
Time series analysis is big passion of mine. This video is probably the most useful one I've seen so far!
If you love Time Series, you should go for Machine Learning. They are competitors. I might be biased, but my view is that Machine Learning is literally eating Time Series Econometrics. In Forecasting problems, it was a time that Time Series Econometrician used to reign. Now, Neural Network dominates completely.
@@causaldeeplearning4738 do you recommend the machine learning modules in dataquest?
@@bacharahmad2277 Yes, I do recommend. I will teach again Machine Learning in some of my courses. I intend to use the Machine Learning modules of Dataquest.
That is a pretty advanced video! If you allow me to give feedback, I would recommend splitting the video into two shorter videos: the first would be the theoretical explanation of the differences between time series, cross-section, and panel data and the second would be the application on Python and documentation examples. The first video will be suitable for all users while the second will be a little bit more specific to data science students
I agree with you. I recorded this video, because Stephanie asked me the difference among Python Series, Dataframe, and Panel. I posted this same video under different title. I have a video comparing only two categories and not three.
@@causaldeeplearning4738 And I am immensely grateful for this video!
Thanks a million! Do more work in-line with the fundamentals, this domain is a fast moving one and lot of assumptions are made without understanding the fundamental difference between type of techniques and when is best to use one over the other.
Thanks, will do!
When putting together a model to analyze current stock prices and project future prices, I imagine analysts prefer to use time-series data.
Day traders like to do this (use historical prices), but according with many academic papers, they lose money.
So when data of many subjects are observed at the same time they're called cross section and when data contains measurement over multiple time periods it's called panel data? Would that be accurate to try to remember it that way?
Panel data is when you observe the same entities (people, firms, countries) over different period of time. It must be the same entities, otherwise it will not be a panel data.
thank you, this is excellent! would you characterize Panel Data as essentially a time series for multiple individuals/entities/etc.? or it is more nuanced? because it seems to me like this type of data would allow us to compare the change in a given variable across time for many individuals which would be the same as if we had time series data for each individual and we can draw comparisons, is that more or less correct?
There is an important nuance. A crucial characteristic of panel data is that the same unities of analysis (ex: same people, firms, countries, etc.) are observed over time. The consequence is that you can eliminate confound factors that don't vary over time.
What is the difference between pooled cross sectional data and panel data? The seem so similar to me.
Panel Data: Observe cross-sections of the same individuals at different points in time. Ex: National Longitudinal Survey of Youth (NLSY).
Pooled Cross-Section data: Randomly sampled cross-sections of individuals at different points in time. The individuals are different at different points in time. Ex: Current population survey (CPS).
Panel Data is far superior to eliminate confound factors that don't' vary over time than Pooled Cross-Section data.