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.’  I recently learned that the ’sum’ command differs by the type of generate command and 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.

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'));

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

Will the recent uptick in oil prices undermine prospects for recovery from the recession?  Retail gasoline prices have risen about 50 cents a gallon from their low in December.  That takes away about $70 billion from consumers’ annual spending power, which is hardly helpful for the broader challenge of restoring household balance sheets to a level where spending could be expected to pick back up.  But let me emphasize that although I believe that the initial spike in oil prices was an important element of the process that produced our current difficulties, we are currently at a point at which the multipliers and spillovers associated with the recession dynamic itself have become far more important factors than the price of oil.  The problems faced by U.S. automakers at the moment– and those problems are very, very daunting– are not caused by the price of gasoline.  What is needed to restore U.S. vehicle sales now is not lower gas prices but instead more income, jobs, and confidence on the part of consumers.

 Notwithstanding, the recent rise in oil prices again underscores the present reality of the long-run challenges.  Even if we see significant short-run gains in global oil production capabilities, if demand from China and elsewhere returns to its previous rate of growth, it will not be too long before the same calculus that produced the oil price spike of 2007-08 will be back to haunt us again.

James Hamilton explains that the increases in oil prices from 2006 – 2007 go a long way towards explaining our current recession.

Posted via web from Michael’s posterous