1. Install Growl and leave the disk image open.
2. Open Terminal.
3. Run “cd /Volumes/Growl-1.2/Extras/growlnotify”
4. Run “./install.sh”

Then follow these directions to get your R script to talk to Growl by adding something like:
system(paste("growlnotify -a R -t \"R is done\" -m", "\"Inserted the data\"", sep=""))


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.

1. Select the subset of the data you want (e.g. US academic) and download the XML file.
2. Fix some validation errors: take out the “<” and “>” within the text of nodes (I use TextMate for this).
3. Parse the XML file with a custom PHP script that creates a csv file with school, position, location and url to posting. Here is my simple script for the academic XML file.
4. Save the csv file produced by the script in step 3 as an Excel spreadsheet (Google Docs doesn’t like csv’s).  Add a “Latitude” and “Longitude” column to the spreadsheet.
5. Upload the Excel file to Google docs.
6. Follow these directions to populate the latitude and longitude of each position+location.
7. Publish the Google spreadsheet and save the unique id in the url that Google gives you.
8. Sign up for a Google Maps API account.
9. Follow these directions to produce a Google map of your postings.

UPDATE: This service may make this process a bit easier, produce cleaner maps and allow the incorporation of more information.

Maybe the AEA can follow these directions to produce these maps after this year.  Contact me with any suggestions or questions.

add_path('export_fig')

to your m file and you can use the function. I had the best luck (see example below) with the follow command:

export_fig('figures/updates/risk_over_size.png', '-png', '-nocrop');


names = cell(3,2);
names(high_regime,: ) = [{'Probability of a home run'} {'Home Run'}];
names(low_regime, : ) = [{'Probability of bankruptcy'} {'Bankruptcy'}];
names(middle_regime, : ) = [{'Probability break-even'} {'Break-even'}];

If you want to access a particular element of this cell array as a string, you must use the curly brackets like so:

set(plot1(1),'LineStyle','-.','DisplayName',names{1,2});

If you try the standard names(1,2), the function set() will not think the result is a string.

It is clear from the figure that the return regimes separate nicely into the outcomes “high,” “medium” and “low.”  Venture capitalists like to call the outcomes in their portfolios “home runs,” “singles” or “strikeouts” and they typically set goals for proportions of each in their portfolio.  The mean log returns and volatilities for each regime show extreme separation between the two tails.

Distribution of Returns by Regime

Regime			Probability
Home run	231%	123%	20%
Break-even	-1%	80%	60%
Bankruptcy	-273%	137%	20%
Full Model	-9%	112%	N/A

Includes all returns observations.  Estimated with sample selection and endogeneity corrections.

The mixing probabilities are a function of lagged capital stock, so I can plot the probability of each outcome for a range of dollars invested.  Figure 2 below shows that the bankruptcy risk is constant across capital stock while the probability of a home-run is highest for small firms.  Similarly, as firms raise more capital (and thus avoid bankruptcy) the most likely outcome becomes the “break-even” state with a 0% return.

Tomorrow I will discuss the motivation — theoretical and statistical — for the mixture model and parameterization of the mixing probabilities.

My work on VC risk and return currently focuses on fitting a mixture model to the selection-corrected round-to-round returns data. This model can incorporate non-normality, skewness, kurtosis and outliers. Recently, I introduced lagged capital stock into the mixing probabilities through a multinomial logit model because analysis of the full model on “small” and “large” firms illustrated significant differences in results across firm size. With a continuous variable like capital stock, I can produce the estimated mixture pdf for a wide range of entrepreneurial firm sizes. The video below shows the progression of the selection and endogeneity-corrected (they are different!) mixture pdf.

[vimeo]http://www.vimeo.com/6393464[/vimeo]

The most dramatic change as firm size increases is in the right tail: larger firms have significantly more mass in the middle of the distribution. The underlying regimes match a world of “Losers,” “Winners” and “Break Even” as seen in the figure below.

3-regime VC returns

I have discovered that incorporating lagged capital stock into the mixing probability helps to separate the individual regimes. I will be posting some more information about my results later in the week.

date_now = clock;
date_now = strcat(num2str(date_now(1)),'_',num2str(date_now(2)),'_', num2str(date_now(3)), num2str(date_now(4)), num2str(date_now(5)));
diary(strcat('log', date_now,'.log'));