This is from work with an Economics thesis student; s/he was conducting a timeseries analysis on crop data. The crop data started as a *.csv (comma-separated values) file. Below you can find explanation of the steps as well as code used to carry out analysis.
1) Bring the data in to Stata, from the *.csv format. import delimited "/Users/bottk/Downloads/crop_price.csv"
2) Generate a new datetime variable.
(Note: datetime can be pretty complicated. Read the datetime documentation for background.)
In this case, a variable “month” existed in the dataset that was in the format M20Y. (So, for example, the dates were entered as “Sept00”, “Oct01” — with month (M) stated explicitly, assuming a year of “20__” and ending in the stated last two digits.) The below code generated a new variable called “eventdate2”. The “monthly” notation tells Stata where to find the different pieces of the date information. gen double eventdate2 = monthly(month, "M20Y")
3) Format the variable you just created.
Once you have generated the new variable from the old information, you need to set that variable to a specific datetime format. This can be tricky; see datetime in the Stata help documentation (type “help datetime” at the command line in Stata) for details.. format eventdate2 %tm
4) Declare your data to be time series data
Now you have a time variable that Stata understands — so you are ready to define your timeseries. Use the variable you have created (in this case, “eventdate2”) to set that time series.. tsset eventdate2
5) Visualize your time series
Graphs can provide an accessible way to assess data — moreso than tables. “tsline” is a time-series specific line graph.. tsline wheat_price rice_price