https://journals.sagepub.com/doi/pdf/10.1177/1536867X1101100112

Stata tip 96: Cube roots

Stata data-management reference manual

Spells in time series (and more generally in any kind of one-dimensional series) may be dened as sequences of observations that are homogeneous in some sense. For example, a categorical variable may remain in the same state, or values of a measured variable may satisfy the same true{false condition. Devices for working with spells in Stata include marking the start of each spell with indicator variables and tagging spells with integer codes. Panel data are easy to handle with the by: prex. Some kinds of spell identication require two passes through the data, as when only spells of some minimum length are of interest or short gaps are tolerable within spells. Many questions concerning spells are easy to answer given careful use of by: and appropriate sort order, selection of just 1 observation from each panel or spell, and appreciation of the many functions written for egen. Gaps before, between, and after spells can also be important, and I suggest a convention for handling them.

/pdf/speaking-stata-identifying-spells-5fncq0hu6k.pdf

Speaking Stata: Identifying spells

Functions are the unsung heroes of Stata. This column is a tour of functions that might easily be missed or underestimated, with a potpourri of tips, tricks, and examples for a wide range of basic problems.

https://journals.sagepub.com/doi/pdf/10.1177/1536867X1101100308

Speaking Stata: Fun and fluency with functions

The uniform() function generates random draws from a uniform distribution between zero and one ([D] functions). One of its many uses is creating random draws from a discrete distribution where each possible value has a known probability. A uniform distribution means that each number between zero and one is equally likely to be drawn. So the probability that a random draw from a uniform distribution has a value less than .50 is 50%, the probability that such a random draw has a value less than .60 is 60%, etc. The example below shows how this can be used to create a random variable, where the probability of drawing a 1 is 60% and the probability of drawing a 0 40%. In the first line random draws from the uniform distribution are stored in the variable rand. Each case has a 60% probability of getting a value of rand that is less than .60 and a 40% probability that it receives a value more than .60. The second line uses this fact to create draws from the desired distribution. Using the cond() function (Kantor and Cox 2005) it creates a new variable, draw, which has the value 1 if rand is less than .6 and 0 if rand has a value more than .60.

/pdf/stata-tip-48-discrete-uses-for-uniform-vqz2x79e7s.pdf

Stata tip 48: Discrete uses for uniform()

This is a tutorial on the cond() function, giving explanations and examples and assessing its advantages and limitations.

https://journals.sagepub.com/doi/pdf/10.1177/1536867X0500500310

Depending on conditions: a tutorial on the cond() function

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Depending on conditions: a tutorial on the cond() function