A very helpful subsciber presented a solution very similar to one that I mocked up. With some of the code posted on the Statalist and some new additions, I present odbc2create. UPDATE: I fixed an issue when dealing with a database with no dates and had to add a loop.

This modified odbc command does the following:

• searches all your variables for dates (they must be formatted as such or Stata’s ability to detect them is impossible)
• converts those dates to the YYYY-MM-DD format
• inserts the dates into your mysql database as strings
• re-types those date columns in the newly created database as DATEs

The best part: when you load a table created this way back into Stata, it immediately recognized the DATEs as dates.  I hope they build this functionality into Stata in the future.  One caveat (which may explain why they haven’t built it internally) is that the ALTER command in the ado file is specific to MySQL.  Someone should generalize the code to recognize the datasource engine and modify the ALTER command accordingly.

Regression Table with Multiple Equations and Stages

Here I run a couple of limited dependent variable models and a two-stage bivariate probit with an IV. The output isn’t perfect, but it works for pre-submission distribution.

The output looks like this:

Latex regression output from eststo

Summary statistics with a by variable

Next, consider summarizing the characteristics of two groups in your data. For example, I want to compare the age, number of boards seats and other features of venture capital spinoff founders to everyone else.

The output will look like this:

Found

The full details of the esttab, estpost and eststo have many more options and a lot of examples.

firm_id,date,amount

and you want to create a new variable that is the total amount as of each date for each firm.  In Stata, you simply type:

sort firm_id date
bysort firm_id: gen total_t = sum(amount)

Note the use of ‘gen‘ rather than ‘egen.’ The ‘sum’ command differs by the type of generate command (i.e. gen or egen), so about 500 lines of loops written in Stata code could be condensed in a few lines.  Stata needs to fix the ‘egen’ and ‘gen’ distinction or I need to port more of my projects to R.

Make sure that you set your column collations to latin1_swedish_c if you want to load a mysql table not created by Stata.

Although Stata has powerful lag operators for both time series and panel settings, it is difficult to construct lagged sums of variables across an unbalanced panel.  Suppose you have observations for individuals.   Each time period you observe some value like investments, hours worked, etc.   The example I discuss below simply counts the number of times an event has occurred up to time for each observation.  Here an event is “IPO” where I need the total number of IPOs prior to $latex  t$.  First, I  construct an indicator for the event and generate a simple date variable that ignores the unbalanced nature of the dataset:

gen ipo = (outcome == "IPO")
sort panelid datevar
bysort panelid: gen date = _n
sum date
local total = r(max)
keep panelid date ipo

I save this as a temporary dataset for the unfortunate for loop:

save temp, replace

and reshape for lagged summing:

reshape wide ipo, i(panelid) j(date)

The following for loop exploits the wide nature of the reshaped data and computes a sum of the IPO variable as of date :

forvalues x = 2(1)`total'{
if(`x'==2){
gen total_ipo2 = ipo1
}
else {
local minx = `x'-1
gen total_ipo`x' = total_ipo`minx' + ipo`minx'
}
}

Now have to reshape back

drop ipo1-ipo`total'
reshape long total_ipo, i(panelid) j(date)
drop if total_ipo == .
sort panelid date

The sums in the for loop above generate one too many observations at the end of each observations time series that we need to drop:

bysort panelid: gen n = _n
bysort panelid: gen nn = _N
drop if n == nn
drop n nn
keep panelid date total_*
sort panelid date

And it is done.  I am sure that there is a more elegant solution….

kdensity income if year == 1990

Next, we append this command with the ‘addplot‘ function:

kdensity income if year == 1990, addplot(kdensity income if year == 1991)

and we can add even more with the ‘||’ syntax:

kdensity income if year == 1990, addplot(kdensity income if year == 1991 || kdensity income if year == 1992)

If we could use the ‘by’ option, this process would be much cleaner. Finally, we add a legend:

kdensity income if year == 1990, addplot(kdensity income if year == 1991 || kdensity income if year == 1992) legend(ring(0) pos(2) label(1 "1990") label(2 "1991") label(3 "1992"))

encode your_string_variable, gen(new_string_as_int)

The labels in the edit/browse view will look like strings, so make sure you use ‘nolabel’ on any export or tab commands.

Download: escape_all_quotes

The script loops through all the variables in your dataset, checks if each is a string and if so, escapes any quotes. Although it has worked for 1000s of my “odbc insert”s, their could be bugs or speed improvements. Let me know.