RT Dissertation/Thesis
T1 Essays on econometric methods for duration data analysis
A1 García Suaza, Andrés Felipe
AB In economic analysis is usual to find that the outcome of interest representsthe duration until an event occurs, e.g. the duration until getting a job, the firms lifetime, among others. The major challenge to analyze duration or survival datais the presence of censoring. The most of the existing survival models usuallyassume a parametric or semiparametric conditional hazard function. This thesis isformed by three chapters regarding alternative semiparametric estimation methodssuitable for survival times observed under random censoring that do not require assumptions on the underlying duration distribution. These methods are motivatedand applied in the context of unemployment duration studies.Chapter 1 studies counterfactual decomposition methods. Existing inferenceprocedures applicable when data is fully observed, might produce missleading conclussions. This may explain the lack of decomposition exercises for variables related to duration outcomes, typically observed under right censoring. We proposetwo decomposition methods that consider the presence of this kind of censoring. First, under suitable restrictions on the censoring mechanism, we providean Oaxaca-Blinder type decomposition method of the mean in a nonparametriccontext. Consistent estimation of the decomposition components is based on aprior estimator of the joint distribution of duration and covariates. Secondly, weconsider a method that makes possible to decompose other distributional features,such as the median or the Gini coefficient. To do so, weaker assumptions on thecensoring nature are needed, but it is required to introduce restrictions on thefunctional form of the conditional distribution of duration given covariates. Weprovide formal justification for asymptotic inference and study the finite sampleperformance through Monte Carlo experiments. Finally, we apply the proposedmethodology to the analysis of unemployment duration gaps in Spain. This study suggests that factors beyond the workers' socioeconomic characteristics play a relevant role in explaining the difference between several unemployment durationdistribution features such as the mean, the probability of being long term unemployed and the Gini coeficient.Chapter 2 proposes inference procedures on distributional regression models inthe context of survival analysis. These models generalize classical survival modelsto a situation where slope coeficients depend on duration time. We formally justifyasymptotic inferences on the varying coeficients under weak regularity conditions,similar to those needed when data is not censored. Finite sample properties of theproposed inference procedures are studied by means of Monte Carlo experiments.Finally, proposed method is implemented in two empirical exercises using US data.First, we study the effect of unemployment benefits on unemployment duration;and secondly we perform a counterfactual decomposition in the context of therecent Great Recession using US data.Chapter 3 adapts the generalized method of moments (GMM) to estimatingparameters identified by moment restrictions involving survival time observed under right random censoring. When the underlying nonparametric joint distributionof survival time and the rest varibles can be identified under random censoring, themoment restrictions can be consistently estimated by weighting averages, whichform a basis for the proposed GMM. Under classical assumptions in GMM estimation, we show consistency and asymptotic normality, and provide the optimalweighting matrix that maximizes relative efficiency. Finite sample properties arestudied using a Monte Carlo expertiment of a linear in parameter structural model.
YR 2016
FD 2016-06
LK https://hdl.handle.net/10016/23467
UL https://hdl.handle.net/10016/23467
LA eng
DS e-Archivo
RD 16 jun. 2024