Nonparametric estimates of hazard rates can be computed as functions of time (e.g., age or calendar time). Given random variations in survival times, estimates of the hazard typically must be smoothed to distinguish trends from noise. Left truncation (at a known age or time) and right censoring typically complicate estimation. Stata does not include routines to estimate smoothed hazards. Therefore, I will present a practical means to estimate smoothed hazards, allowing for possible left-truncation and right-censoring. The presentation will consider the use of kernel density estimation methods. For discrete time intervals of fixed length, other approaches are available, and may be mentioned. Confidence intervals and choice of (constant and varying) smoothing window widths may also be discussed. Company exit ("death") rates for alternative types of firms will be used as illustration.
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