help for ^stpiece^

Estimation of piecewise-constant hazard rate models ---------------------------------------------------

^stpiece^ [varlist] [^if^ exp] [^in^ range], [^tp(^numlist^)^ ^tv(^varlist^)^ ^pre^split^(^#^)^ ^nopre^serve ^streg_options^]

Description -----------

^stpiece^ is a wrapper that uses a number of existing Stata routines to estimate piecewise-constant hazard rate models. A piecewise-constant model is an exponential hazard rate model where the constant rate is allowed to vary within pre-defined time-segments. See the discussion in, for example, Blossfeld and Rohwer (1995). Estimation of this model in Stata is usually a multi-step process, since one must manually define the time pieces and if necessary split the spells. ^stpiece^ automates this process.

^stpiece^ allows you to define the time intervals (time pieces) with the ^tp^ option and then splits the data for you using @stsplit@. In addition, ^stpiece^ will allow you to specify, with the ^tv^ option, variables whose effects you think may be non-proportional, i.e. they may vary between time pieces.

By default, ^stpiece^ will @preserve@ your data before creating the time pieces. This is typically convenient as it leaves your data "as is," thereby avoiding any problems that can arise when you split the spells and then decide to change your outcome variable. However, it does slow the program down, since the data must be re-split before each model estimation. The ^nopreserve^ option will cause ^stpiece^ to change the data. You can then use the ^presplit^ option to avoid having to split the data again.

The data must be ^st^. In order to create time pieces, an id variable must be declared with ^stset^. See @st@ and @stset@.

Options -------

^tp()^ allows you to define the time periods using a numlist. The logic is the same as for ^stsplit^. See @stsplit@ for details. The time pieces are listed in the output as tp#, where # indexes the time pieces sequentially, starting at 1.

^tv()^ allows you to specify variables whose effects vary between time pieces. These variables appear in the output as %stub%tp#, where %stub% is the first four letters of the variable name, and # indexes times pieces sequentially, starting at 1. For example, specifying ^tv(^educat^)^ for a model with four time pieces would result in variables labelled eductp1 - eductp4. The user must be sensitive to the possibility of variable name comflicts.

^presplit(#)^ is useful if the data has already been split and the time pieces have already been defined. # specifies how many time pieces exist. In this case, the variables identifying the time pieces must follow the convention defined by the option ^tp^. In other words, the time period dummy variables must be of the form tp#, starting at tp1 and increasing sequentially. Note that it is possible to combine ^tv^ with ^presplit^.

^nopreserve^ tells ^stpiece^ that you are willing to let it change your data. There are two advantages: 1) speed (particularly in large data sets); and 2) you retain the variables created by stpiece after it finishes.

Examples --------

.^stpiece sex race, tp(0,1,3,10) tv(educ)^ .^stpiece sex race, tp(0(5)25) tv(educ)^ .^stpiece sex race, tv(educ) pre(4)^

References ----------

Blossfeld, Hans-Peter, and Gotz Rohwer. 1995. Techniques of Event History Modeling. Mahwah, NJ: Lawrence Erlbaum.

Note ----

The parameter estimates generated by stpiece will not match exactly with the results produced by Transition Data Analysis (TDA). This is due to a minor difference between TDA and ^stsplit^ in how they define time pieces. Specifically, TDA defines time pieces in the manner [t0,t1) while ^stsplit^ defines time pieces in the manner (t0,t1]. The estimates can be made to agree by subtracting a very small constant from the finishing times when using ^stpiece^, but all that is achieved by this is making the software packages agree.

Author ------

Jesper B. Sorensen University of Chicago Graduate School of Business email: jesper.sorensen@@gsb.uchicago.edu