Using Stata 14 Serial 32
In addition to the readings, there are 6 graded problem sets and ungraded review problem sets at the beginning and end of the course. The problem sets have both analytical and computer-exercise components. The statistical analysis will be done using Stata or SAS on PCs or MIT workstations. Help for new Stata users will be given in recitation.
using stata 14 serial 32
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The PDF manuals are loaded by a script named stata_pdf, located inside your Stata installation directory (eg. /usr/local/stata15/stata_pdf).By default the script points to Acrobat Reader (acroread), but Adobe discontinued it around 2014 (good riddance). We can make some edits to stata_pdf so that it uses Evince, which is the default PDF reader in many Linux distros.
Longitudinal data are data containing measurements on subjects at multipletimes. Visualizing longitudinal data without loss of data can be difficult, but there are several ways to do so in Stata. Both Stata command xtline and Stata user-written command profileplot (see How can I use the search command to search for programs and get additional help? for more information about using search) allow you to generate plots with time on the horizontal axis and levels of an outcome on the vertical axis, connecting the related points (points from a single subject or from a specified group) with a line. This type of plot allows you to trace the levels of the outcome variable over time for a given subject and can often reveal larger patterns that may be of interest. Which command is appropriate for your dataset depends on the shape of your dataset.
When the number of variables in a data set to be analyzed with Stata is larger than 2,047 (very likely with large surveys), the dataset is divided into several segments, each saved as a Stata dataset (.dta file). In order to work with information contained in two or more .dta files it is necessary to merge the segments into a new single file which must not contain more than 2,047 variables. Here is a list of steps to construct a new database with information merged from different files. Recall that any manipulation of the data made with a Stata do-file allows you to review and/or repeat the procedure more easily, an example of how to make a do-file is given below.1. Review the codebook or list of variables and determine what information is needed and which files contain the desired variables.2. Read into Stata the first file, or segment:use filename.dta Note that an unique ID for each case (observation) must be provided in each file to be merged. Typically the ID for a time series database is the date of the observation. For a cross section, it is the ID of the cross section unit (family identifier, firm CUSIP, etc.) , and in panel data two characteristics are needed to identify each observation: date and ID. However for panel data, sometimes a "case ID" is provided to facilitate merging.It is important to ensure that the form in which the unique ID is held in each file must match: i.e. you can not match a "str8" (8-character string) to an "str6" ID, nor can you match a string toan integer. Use Stata's "describe" command to ensure that the name and data typeof the ID variable are the same in all files. 3. Discard the variables that are NOT needed (keeping the case ID); this can be done in at least two ways. Wildcards (*) and hyphens (-) may be used in the varlists; see "help varlist" for theiruse.if the useful variables can be listed more easily:keep caseID varname1 varname2 .... varnameNif the unwanted variables can be listed more easily:drop varname1 varname2 ..... varnameNRemember that the case ID must be part of the new file. 4. Verify that only the desired variables are in memory:desc5. Sort the data by case ID:sort caseID6. Save the sorted data currently in memory with a different name: save newfile#.dta7. Repeat steps 2 to 5 for all files containing the desired variables. Finally you will end up with a set of new files (newfile1.dta, newfile2.dta, .... newfileJ.dta)to be merged into a new dataset. Now you are ready to merge the data.The merge command merges corresponding observations from the dataset currently in memory (called the master dataset) with those from a different Stata-format dataset (called the using dataset) into single observations. A new variable _merge is created for informative purposes (described below). Both files must be previously sorted by the merge variable(s), e.g. case ID. 8. Merge the first two new files.a) Read the master dataset (newfile1.dtarecently created):use newfile1.dta, clearb) Merge the data with the using dataset (newfile2.dta):merge caseID using newfile2.dtac) Tabulate _merge:tab _mergeThe variable _merge is created automatically and it takes the following values:_merge==1 if the observation was taken from the master data only _merge==2 if the observation was taken from the using data only _merge==3 if the observation match both master and using data You can use the tabulated information to check if the data were merged as desired.d) Drop the _merge variable:drop _mergee) If there are more than two files to be merged, use the current data in memory as the master dataset and repeat steps 8b-8d for each file to be merged (newfile3.dta, newfile4.dta, .... newfileJ.dta). 9. Save the new dataset:save newdataset.dtaSample programHere is an example of how a do-file can be used to merge data contained in three hypothetical segments. Variables to merge: X11, X12, X13, X21, X22, X23, X31, X32 and X33
Segments containing these variables: segment1.dta, segment2.dta and segment3.dta
Identifier: ID (the variable ID, contained in each of the three segments)
This do file merges some variables from the .dta files: segment1.dta, segment2.dta and segment3.dtainto a new file named newdatabase.dta. This do-file will be documented in the log-file logmerge.smcl for further reference.
You need to work with HIPAA data or other sensitive data. Silo is SSCC's secure computing enclave, combining secure data storage and secure servers that are very similar to Winstat and Linstat. You can connect to it from any location using your own computer, but all the data and computation stay in the secure environment.
To start Stata on Winstat or another Windows computer, type Stata in the search box next to the Windows Logo button, or click on the button and find Stata in the programs list. On Linstat, type xstata.
The menus above the toolbar give you access to most of Stata's commands and a graphical user interface for running them, but you'll work much faster if you type them instead. There are some situations where the menus are useful. Importing data from non-Stata formats is one, because the graphical user interface will give you a preview of the data you can use to figure out the correct options for importing it. Making graphs is another, because there are so many options for graphs that setting them using the graphical user interface is a good alternative to memorizing them all. When you do something using the menus, Stata will craft a command based on what you chose and add it to the history just as if you'd typed it. You can then paste the command into a do file to make it reproducible.
This short note discusses two alternative ways to model dynamics in happiness regressions. A explained, this may be important when standard fixed effects estimates have serial correlation in the residuals, but is also potentially useful when serial correlation is not a problem for providing new insights in the happiness of economics area. The note discusses modelling dynamics two ways the note discusses are via a lagged dependent variable, and via an AR(1) process. The usefulness and statistical appropriateness of each is discussed with reference to happiness. Finally, a flow chart is provided summarising key decisions regarding the choice regarding, and potential necessity of, modelling dynamics. 350c69d7ab
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