TY - JOUR
AU1 - Jenkins, Stephen P.
AB -  Easy Estimation Methods for Discrete-Time Duration Models Stephen P.Jenkinst I. INTRODUCTION empirical analysis of duration data, discrete-time models have several over continuous-time models. For example, one can straightforwardly estimate - without writing special programs - discrete-time models combining both time-varying covariates and flexible specifications of duration dependence.' The recent introduction of duration analysis modules into widely-available econometric software packages has reduced these advantages, but these modules remain limited. In particular, no module allows modification of the likelihood function to take account of the Of sampling scheme used.? This paper shows how the "'serious but occasional" applied econometrician' (MacKie-Mason, 1992, p. 165) Can estimate discrete-time duration models taking account of some empiricallyImportant sampling schemes and do SO using readily-available packages. Although inferences about some underlying population are the usual goal, data are often derived, not from a random sample of that population, but from a sample of people flowing into or out of a particular state (flow 'ampling) Or from a sample of those occupying a state at a given time (Stock If we do not control for this - e.g. by continuing to use existing BULLETIN package estimation modules - estimates may be contaminated by a form of 
TI - Easy Estimation Methods for Discrete‐Time Duration Models
JF - Oxford Bulletin of Economics & Statistics
DO - 10.1111/j.1468-0084.1995.tb00031.x
DA - 1995-02-01
UR - https://www.deepdyve.com/lp/wiley/easy-estimation-methods-for-discrete-time-duration-models-h1PFmLD1pm
SP - 129
VL - 57
IS - 1
DP - DeepDyve
ER -