Exploration of time series data

Estimated time 60 minutes plus independent work.
In this lab we will begin to explore time series data and series operations such as smoothing,
differencing, and using lags in a model.
Part 1 Data Smoothing and filtering

1. Please open the lab6bdta. These data were obtained from the EIA and U.S. BEA and contain
   information on energy consumption by fuel and inflation adjusted GDP expressed as an index
   number from 1948 to 2020
2. Stata like time series data to be in a table with the rows representing years and the columns
   representing the variables in the model. The data from the EIA are already in this format but I
   had to join them with the BEA data using the “transpose” function in excel because the BEA
   reported GDP with each year being a column and the components of GDP in the rows.
   Although I have prepared the data for you already, if you are not familiar with the copy and
   transpose function please take a moment to look it up. In either case include a brief instruction
   on how to copy and transpose in excel in your writeup for future use.
3. With the datafile open it is now necessary to tell the software that these are time series data.
   This is done using the command .tset , use help to look this command up and write out the basic
   command syntax.
4. Visualize the GDP and total energy consumption. Since these are time series data a scatter plot
   may not be very useful. .tsline will generate a line plot , generate line plots of GDP and total
   energy consumption then use the .twoway prefix as we did in prior labs to plot GDP and total
   energy together. Assess your plots, do either suggest a time trend? Describe the general trend
   and the shape of these data trends. What function would best describe them?
5. Make a log transform of the GDP data to see if it produces a more linear pattern using .gen .
   Label your new transformed variable lgdp. Plot this new variable on its own and with total
   energy consumption. Based on visual analysis did this transformation provide any useful
   insights?
6. Let us try applying a moving average smoothing transformation to these data using .tssmooth.
   Use help to learn the syntax of this command. (You may have noticed the time series commands
   start with ts, for time series.)
7. Create a 5 year moving average of GDP using .tssmooth, call the new smoothed variable sm1
   (smooth one ). Compare the smoothed and raw GDP trends.
8. Try to use one other type of smoother described in your textbook, label it sm2 and compare the
   results with sm1.
   Part 2 Time Series Regression Intro
   Please open the lab6 data in STATA. This file contains a reduced form of some data I generated for my
   research. The data are both spatial, by state, AND time series. Remember that data that are time series
   and have set identifiers, in this case a spatial id, are called panel data. Note that panels need not be
   spatial, if you had data by time and any other grouping it is still a panel. This could be research plots,
   population groupings, or cohorts, among many other types of groupings. In this lab we will consider
   the special considerations that need to be made when we work with this type of data.