格林面板数据讲义20ahazardmodels课件

1、格林面板数据讲义20ahazardmodels课件格林面板数据讲义20ahazardmodels课件Econometric Analysis of Panel Data20A. Hazard and Duration ModelsEconometric Analysis of Panel Modeling DurationTime until business failureTime until exercise of a warrantyLength of an unemployment spellLength of time between childrenTime between bus

2、iness cyclesTime between wars or civil insurrectionsTime between policy changesEtc.Modeling DurationTime until buHazard Models for DurationBasic hazard rate modelParametric modelsDuration dependenceCensoringTime varying covariatesSample selectionHazard Models for DurationBasiThe Hazard FunctionThe H

3、azard FunctionHazard FunctionHazard FunctionA Simple Hazard FunctionA Simple Hazard FunctionDuration DependenceDuration DependenceParametric Models of DurationParametric Models of DurationCensoringCensoringAccelerated Failure Time ModelsAccelerated Failure Time ModelProportional Hazards ModelsPropor

4、tional Hazards ModelsEstimationEstimationTime Varying CovariatesTime Varying CovariatesUnobserved HeterogeneityUnobserved HeterogeneityInterpretationWhat are the coefficients?Are there marginal effects?What is of interest in the study?InterpretationWhat are the coeA Semiparametric ModelA Semiparamet

5、ric ModelNonparametric ApproachBased simply on counting observationsK spells = ending times 1,Kdj = # spells ending at time tjmj = # spells censored in interval tj , tj+1)rj = # spells in the risk set at time tj = (dj+mj)Estimated hazard, h(tj) = dj/rjEstimated survival = 1 h(tj) (Kaplan-Meier “prod

6、uct limit” estimatorNonparametric ApproachBased siKennans Strike Duration DataKennans Strike Duration DataKaplan Meier Survival FunctionKaplan Meier Survival FunctionHazard RatesHazard RatesHazard FunctionHazard FunctionWeibull Model+-+| Loglinear survival model: WEIBULL | Log likelihood function -9

7、7.39018 | Number of parameters 3 | Akaike IC= 200.780 Bayes IC= 207.162 |+-+-+-+-+-+-+-+|Variable | Coefficient | Standard Error |b/St.Er.|P|Z|z | Mean of X|+-+-+-+-+-+-+ RHS of hazard model Constant 3.82757279 .15286595 25.039 .0000 PROD -10.4301961 3.26398911 -3.196 .0014 .01102306 Ancillary param

8、eters for survival Sigma 1.05191710 .14062354 7.480 .0000Weibull Model+-Weibull Model +-+ | Parameters of underlying density at data means: | | Parameter Estimate Std. Error Confidence Interval | | - | | Lambda .02441 .00358 .0174 to .0314 | | P .95065 .12709 .7016 to 1.1997 | | Median 27.85629 4.09

9、007 19.8398 to 35.8728 | | Percentiles of survival distribution: | | Survival .25 .50 .75 .95 | | Time 57.75 27.86 11.05 1.80 | +-+Weibull Model +-Survival FunctionSurvival FunctionHazard FunctionHazard FunctionLoglogistic Model+-+| Loglinear survival model: LOGISTIC | Dependent variable LOGCT | Log

10、 likelihood function -97.53461 | Censoring status variable is C |+-+-+-+-+-+-+-+|Variable | Coefficient | Standard Error |b/St.Er.|P|Z|z | Mean of X|+-+-+-+-+-+-+ RHS of hazard model Constant 3.33044203 .17629909 18.891 .0000 PROD -10.2462322 3.46610670 -2.956 .0031 .01102306 Ancillary parameters fo

11、r survival Sigma .78385188 .10475829 7.482 .0000+-+| Loglinear survival model: WEIBULL | Log likelihood function -97.39018 | Number of parameters 3 |Variable | Coefficient | Standard Error |b/St.Er.|P|Z|z | Mean of X|+-+-+-+-+-+-+ RHS of hazard model Constant 3.82757279 .15286595 25.039 .0000 PROD -

12、10.4301961 3.26398911 -3.196 .0014 .01102306 Ancillary parameters for survival Sigma 1.05191710 .14062354 7.480 .0000Loglogistic Model+-Loglogistic Hazard ModelLoglogistic Hazard ModelSample SelectionSample SelectionBuilding a Likelihood for a Weibull Duration Model with SelectionBuilding a Likelihood for a WeBuilding the LikelihoodBuilding the LikelihoodConditional LikelihoodConditional LikelihoodWeibull Model with SelectionStrategy: Hermite quadrature or maximum simulated l